Source: https://patents.google.com/patent/JP4538845B2/en
Timestamp: 2020-06-03 04:53:24
Document Index: 328795308

Matched Legal Cases: ['art 700', 'Application No. 2001', 'Application No. 2001', 'Application No. 2001', 'art 234', 'art 908', 'art 908', 'art 1', 'art 2', 'art, 13', 'art, 14', 'art, 15', 'art, 16']

JP4538845B2 - Failure diagnosis method, failure diagnosis device, image forming device, program, and storage medium - Google Patents
Failure diagnosis method, failure diagnosis device, image forming device, program, and storage medium Download PDF
JP4538845B2
JP4538845B2 JP2004125676A JP2004125676A JP4538845B2 JP 4538845 B2 JP4538845 B2 JP 4538845B2 JP 2004125676 A JP2004125676 A JP 2004125676A JP 2004125676 A JP2004125676 A JP 2004125676A JP 4538845 B2 JP4538845 B2 JP 4538845B2
JP2004125676A
JP2005309078A (en
2004-04-21 Application filed by 富士ゼロックス株式会社 filed Critical 富士ゼロックス株式会社
2004-04-21 Priority to JP2004125676A priority Critical patent/JP4538845B2/en
2005-11-04 Publication of JP2005309078A publication Critical patent/JP2005309078A/en
2010-09-08 Publication of JP4538845B2 publication Critical patent/JP4538845B2/en
238000003745 diagnosis Methods 0.000 title claims description 268
206010056871 Device failure Diseases 0.000 claims description 6
230000001364 causal effects Effects 0.000 description 8
238000003702 image correction Methods 0.000 description 5
The present invention relates to an image forming apparatus including a copying apparatus, a printer apparatus, a facsimile apparatus, or a conveyance apparatus such as a multifunction machine having the functions thereof, and a driving mechanism section used for other devices (for example, home appliances and automobiles). Failure diagnosis method and failure diagnosis device for diagnosing malfunctions and malfunctions, image forming apparatuses to which the failure diagnosis method and failure diagnosis device are applied, and the failure diagnosis method and failure diagnosis device using an electronic computer (computer) And a storage (recording) medium storing the program.
More specifically, the present invention relates to a technique for automatically diagnosing a conveyance device, an image forming apparatus, and the like by modeling and analyzing a cause causing an apparatus failure, such as an information processing model based on probability, such as a Bayesian network model.
In recent years, various machines such as office machines such as copiers and printers require high productivity, so that delay due to failure is not allowed, and it is required to quickly detect and solve the failure. .
In addition, many other industrial devices such as automobiles, aviation, robots, and semiconductor design devices are equipped with a number of members that are highly reliable and capable of operating at high speed and high accuracy as means for operation control.
In particular, drive members such as motors and solenoids, and mechanism members that operate in conjunction with the drive members include other electronic components (passive electronic components such as resistors and capacitors or transistors) including drive circuits that drive motors and the like. In general, the frequency of failure occurrence is higher than that of an integrated circuit (IC). In particular, when the usage environment is inferior, even if it is used by a normal method, various abnormalities and failures that are difficult to detect occur, and a great deal of labor is required for the repair.
For this reason, a mechanism for performing automatic diagnosis using a rule-based system (rule type system) has been considered. As an example of the rule type system, there is a failure diagnosis system using a Bayesian network (see, for example, Patent Document 1).
JP 2001-75808 A
According to Patent Document 1, a system component that causes a system failure is modeled using a Bayesian network, and the Bayesian network includes a marker node having a state indicating whether or not the system component causes a failure, A plurality of cause nodes each representing a cause of a system component that is coupled to the node and that generates a fault, and each is coupled to at least one cause node of the plurality of cause nodes, and depending on any of the combined cause nodes By having a first plurality of troubleshooting nodes, each representing a troubleshooting step that suggests an action to fix the indicated cause, troubleshooting the system is most likely and necessary to resolve the problem. Smallest action expected cost is to be provided to the user that is.
Here, in the mechanism described in Patent Literature 1, specifically, a service center or the like has a server for a diagnostic system, and a customer connects and exchanges data via the Internet. , The customer's printer is diagnosed using the Bayesian network.
In this example, the customer is troubleshooting in the form of answering the questions of the diagnostic program, but in order to acquire knowledge information, it is recommended that humans observe it directly and look at the printer or printed matter to obtain information and input it Is adopted.
For this reason, in this method, when the observing customer is not familiar with the information, there is a possibility that a large variation occurs in the input information, an accurate diagnosis cannot be performed, and a serious misdiagnosis is caused. Moreover, since many actions are imposed on the user until the problem is solved, the user is stressed. In addition, the cause may not be solved only by the cause indicated before the action is taken.
The present invention has been made in view of the above circumstances, and various information necessary for diagnosing a device when modeling and analyzing the cause of failure, such as using a Bayesian network model, The purpose is to propose a mechanism that can be acquired accurately or without causing stress to the user and that the problem can be reliably solved.
The failure diagnosis method according to the present invention acquires an operation state signal indicating an operation state while each diagnosis target device is operating under different operation conditions, and acquires the acquired operation state signals of the device. By modeling and analyzing the cause of the failure, the failure diagnosis is performed for each component member constituting the diagnosis target device.
The failure diagnosis device according to the present invention is a device suitable for implementing the failure diagnosis method according to the present invention, and the diagnosis target devices acquired in the diagnosis target device operate under different operating conditions. A failure diagnosis unit that performs failure diagnosis on individual components constituting the diagnosis target device by modeling the cause of the device failure and analyzing the operation state signal indicating the operation state during did.
Here, “modeling and analyzing the cause of a device failure” means modeling the cause of a device failure using probability, so that the cause of failure such as the location of failure and the content of failure can be identified. Means to analyze.
Any modeling method may be used as long as it uses a probability. For example, an actual measurement value indicating the state of the device acquired from the device, or a feature amount extracted from the actual measurement value (collectively, both variables) There is a technique to model the dependency relationship between the two. As a specific example , the present invention employs a Bayesian network model (see http://staff.aist.go.jp/y.motomura/bn2002/presen/motomura-tut.files/frame.htm).
Here, the Bayesian network model uses a stochastic model genetic algorithm that generates search points using statistical information of individuals in a population, and probabilistically determines the dependency between nodes (variables). This is an approach to estimate the distribution using a graph structure (called Bayesian network or causal network).
More specifically, first, a random variable (X: {true, false} / {Mon, Tue,...) / Continuous variable) is given to the node. And, for an effective link having a dependency relationship between a parent node and a child node, a graph structure is given for qualitative dependency, and a conditional probability (table / parametric model) is given for quantitative dependency. .
Then, in the fault diagnosis, the failure probability of the nodes constituting the Bayesian network model for analyzing and modeling the cause of failure of the equipment, and specific analyzes automatically obtain information, to the failure probability Based on this, candidates for a location that has a failure or a location that may cause a failure are extracted.
Similarly, when the cause of failure cannot be narrowed down, the information acquired under different operating conditions is used to analyze and identify the information obtained automatically from the failure probability of the node. The candidate of the location which has the possibility of causing the failure or the failure is extracted. In short, the failure probabilities acquired under different operating conditions are comprehensively determined to accurately determine the cause of failure.
The image forming apparatus according to the present invention is configured to include functional elements similar to the functional elements described above that are included in the failure diagnosis apparatus according to the present invention. It should be noted that the invention can also be extracted from the conveyance device provided in the image forming apparatus as being configured to include the same functional elements as the functional elements included in the failure diagnosis apparatus according to the present invention. is there.
Further, the invention described in the dependent claims defines further advantageous specific examples of the failure diagnosis method, the failure diagnosis device, or the image forming apparatus according to the present invention. The program according to the present invention and the computer-readable storage medium storing the program are suitable for realizing the failure diagnosis method and the failure diagnosis apparatus according to the present invention by software using an electronic computer. . The program may be provided by being stored in a computer-readable storage medium, or may be provided by distribution via wired or wireless communication means.
For example, when a failure diagnosis unit is provided in a device connected to a diagnosis target device via a communication line, an operation state signal receiving unit that receives an operation state signal automatically acquired on the diagnosis target device side from the diagnosis target device. It is good to provide.
Further, when the failure diagnosis unit is incorporated in the diagnosis target device (integrated device), the diagnosis target device receives an operation state signal indicating an operation state while the drive mechanism is operating for a predetermined period of time. It is good to provide the operation state signal acquisition part acquired using a member.
Further, the failure diagnosis can diagnose a cause of causing a defect in an output image output from an image forming apparatus as a diagnosis target apparatus. In this case, an image inspection unit that analyzes the state of defects in the captured image by comparing the captured image obtained by optically reading the output image output from the image forming apparatus and the reference image for inspection is further provided. It is preferable to perform failure diagnosis based on the state of the defect analyzed by the image inspection unit.
According to the present invention, the operation state signal indicating the operation state while operating under different operation conditions is analyzed by modeling the cause of the failure of the device, thereby configuring each diagnosis target device. A failure diagnosis was performed on the components. Even if it is difficult to isolate the cause of failure only by automatic diagnosis based on information acquired under one operating condition, a plurality of fault diagnosis can be performed by performing fault diagnosis based on information acquired under another operating condition. Comprehensive determination can be made based on the index, and accurate failure diagnosis is possible.
<< Configuration example of an image forming apparatus equipped with a failure diagnosis apparatus >>
FIG. 1 is a diagram illustrating a configuration example of an image forming apparatus equipped with an embodiment of a failure diagnosis apparatus according to the present invention. The image forming apparatus 1 includes, for example, an image reading unit (scanner unit) that reads an image of a document, and from a copying machine function that prints an image corresponding to the document image based on image data read by the image reading unit, a personal computer, or the like. A multifunction device having a printer function for printing out based on input print data (data representing an image) and a facsimile transmission / reception function capable of printing out a facsimile image, and is configured as a digital printing apparatus. is there.
FIG. 1 shows an image output unit 30 which is a functional part for transferring an image onto a printing paper, which is an example of a conveyance target and a recording medium, and an image reading part which is a functional part for reading an image of a document. Sectional drawing of the mechanism part (hardware constitution) which paid its attention to the part 700 is shown.
<Image output unit>
The image output unit 30 of the present embodiment includes four sets of image forming units that are examples of an image recording device using an optical scanning device (raster output scan; ROS) so as to correspond to each color of K, Y, M, and C. It has a so-called tandem configuration. Hereinafter, reference numerals of the respective members are given reference numerals K, Y, M, and C indicating the respective colors, and the reference numerals are omitted when collectively described.
First, the image output unit 30 includes image forming units 31K, 31Y, 31M, and 31C for K, Y, M, and C, which are juxtaposed sequentially at a predetermined interval in one direction, and a transfer belt (conveying belt) 43. With.
To detect resist patterns (image density and alignment test patches) formed on both sides of the transfer belt 43 above the transfer belt 43 and downstream of the image forming unit 31C in the sheet conveying direction. Pattern detector 614 is provided. In the pattern detection unit 614, for example, three registration correction sensors are arranged in a line in a direction (in the main scanning direction) perpendicular to the conveyance direction of the transfer belt 43.
The transfer belt 43 has not only a sheet conveyance but also a function as a recording body on which a resist pattern is directly printed. When a resist pattern is formed on the transfer belt 43, the pattern detection unit 614 detects the density of the K, Y, M, and C images and the color misregistration amount in the main and sub-scanning directions, and the density control is performed by a correction control unit (not shown). By performing correction, misregistration correction, or image distortion correction, density defects and color misregistration of K, Y, M, and C images on a sheet (paper) are prevented. Since the correction mechanism using the resist pattern is known to those skilled in the art, illustration and detailed description thereof are omitted here.
The transfer belt 43 functions as an intermediate transfer belt, and is sequentially transferred to the transfer belt 43 having the function as the intermediate transfer belt by the image forming unit 31 of each color, and then the transfer belt 43 to which the image is transferred. The image on the transfer belt 43 can be transferred to the sheet by passing the sheet between the transfer roller and the transfer roller.
The image forming unit 31 includes a semiconductor laser 38 and a polygon mirror (rotating polygon mirror) 39 that reflects laser light (laser beam) emitted from the semiconductor laser 38 toward a photosensitive drum roll 32 that is an example of a photosensitive member. The optical scanning device which has these.
Although not shown in the drawing, in addition to the polygon mirror 39, for example, various lenses constituting an optical system such as a collimator lens and a scanning lens, or a half for allowing laser light emitted from the VCSEL 380a to enter the light amount sensor. A mirror or the like is arranged on the optical axis of the laser beam.
Further, the image output unit 30 includes a manual document cassette 41 and a conveyance path 42 for conveying printing paper to the image forming unit 31. A leading edge detection processing unit 44 is provided in proximity to a conveyance path 42 for printing paper conveyed from the document cassette 41 to each image forming unit 31.
The leading edge detection processing unit 44 is provided with, for example, a stop claw (regige) for temporarily stopping the printing paper that has been conveyed on the conveyance path 42 and a regigate solenoid for operating the stop claw. .
A reversing mechanism unit 50 is disposed below the image output unit 30. The reversing mechanism unit 50 includes a built-in paper feed tray 51, a paper reversing mechanism 54 for duplex copying, and a duplex copying path 56. The paper feed tray 51 may have a structure in which a plurality of stages are arranged.
In order to transport the paper from the paper feed tray 51 to the photosensitive drum roll 32, for example, on the transport path 42, as a roll member, a pickup roll (in order from the paper feed tray 51 to the photosensitive drum roll 32K) A nudger roll) 54, a feed roll 55a and a retard roll 55b, a first transport roll pair (takeaway roll pair) 56, a second transport roll pair (pre-registration roll pair) 57, and a third transport. A roll pair (registration roll pair) 58 is provided.
Although not shown, a nudger solenoid for operating the pickup roll 54 is provided in the vicinity of the pickup roll 54. A feed unit 53 is configured by the pickup roll 54, the paper feed roll pair 55, and the nudger solenoid.
The leading edge detection processing unit 44 optically detects the leading edge of the printing paper fed on the transfer belt (conveyance belt) 43 to obtain a leading edge detection signal, and sends the leading edge detection signal to the image processing unit 760. The image processing unit 760 sequentially inputs image forming data of each color of K, Y, M, and C to the image output unit 30 at regular intervals in synchronization with the input tip detection signal.
Information indicating which side of the front / back side of the printing paper should be arranged is input from the image processing unit 760 to the image output unit 30. When the image output unit 30 obtains information to be arranged on the back side, the image output unit 30 sends the printing paper to the reversing mechanism unit 50 side without discharging the printing paper to the outside of the apparatus.
In the image output unit 30 and the reversing mechanism unit 50 configured as described above, first, for example, in the black (K) image forming unit 31K, the semiconductor laser 38K is first driven by a black image forming signal from the image processing unit 760. As a result, the black image forming signal is converted into an optical signal, and the converted laser beam is emitted toward the polygon mirror 39. The laser light further scans the photosensitive drum roll 32K charged by the primary charger 33K via the reflection mirrors 47K, 48K, and 49K, thereby forming an electrostatic latent image on the photosensitive drum roll 32K. .
The electrostatic latent image is converted into a toner image by a developing device 34K to which black toner is supplied. The toner image is transferred by a transfer charger 35K while the document on the transfer belt 43 passes through the photosensitive drum roll 32K. Transferred onto the document. After the transfer, excess toner is removed from the photosensitive drum roll 32K by the cleaner 36K.
Similarly, the semiconductor lasers 38Y, 38M, and 38C are driven by the corresponding Y, M, and C image forming signals obtained from the image processing unit 760 sequentially with respect to the black image forming signals at regular intervals. Thus, the image forming signals of the respective colors are converted into optical signals, and the converted laser light is irradiated toward the polygon mirror 39.
The laser light is further scanned on the corresponding photosensitive drum rolls 32Y, 32M, and 32C charged by the primary chargers 33Y, 33M, and 33C via the reflection mirrors 47Y to 49Y, 47M to 49M, and 47C to 49C. Thus, electrostatic latent images are sequentially formed on the photosensitive drum rolls 32Y, 32M, and 32C.
Each electrostatic latent image is sequentially converted into a toner image by developing units 34Y, 34M, and 34C to which each color toner is supplied, and each toner image is a photosensitive drum roll 32Y, 32M, and the like corresponding to the original on the transfer belt 43. The images are sequentially transferred onto the sheet by the corresponding transfer chargers 35Y, 35M, and 35C while passing through 32C.
The sheet on which the toner images of each color of K, Y, M, and C are sequentially transferred in this manner is peeled off from the transfer belt 43, and the toner is fixed by the fixing roller 45 and discharged to the outside of the copying machine. . A discharge path for discharging the sheet to the outside of the apparatus is provided on the downstream side of the fixing roller 45, and a sheet discharge for receiving the printed sheet on which the image is formed on the print sheet by the image output unit 30 outside the apparatus. A tray 71 is also provided.
In addition, as a configuration for reading an output image used for image diagnosis, a paper width for automatically reading the image in a process in which the paper on which the image is formed is conveyed toward the paper discharge tray 71 outside the apparatus. A line sensor 616 having a length of one minute may be provided between the fixing roller 45 and the paper discharge tray 71 as an image reading unit.
When the double-sided printing mode is set, the printing paper on which the image is fixed on the surface by the fixing roller 45 as described above is guided to the paper reversing mechanism 54 without being discharged out of the apparatus, and the printing paper. Is then fed to the duplex copying path 56. The sheet reversing mechanism 54 and the double-sided copying path 56 are units for making double-sided copying, and when the printing paper is discharged from the fixing roller 45, the printing paper is lowered by a claw (not shown) through the roller 54a of the paper reversing mechanism 54. Then, the roller 54b pushes up the printing paper and guides it onto the double-sided copying path 56 on the right to reverse the printing paper. Thereafter, the toner image for the back surface formed on the photosensitive drum roll 32 is transferred to the back surface of the printing paper through the conveying path 42 so that the back surface faces the photosensitive drum roll 32 side. The
The image output unit 30 sequentially forms an electrostatic latent image of each color of K, Y, M, and C on one photosensitive drum by one laser light scanner, and the electrostatic latent image is formed on the photosensitive drum. Are sequentially formed into toner images by a developing device provided with toners of respective colors K, Y, M, and C, and the toner images are sequentially transferred onto a document adsorbed on a transfer drum. It may be configured.
Further, the image forming apparatus 1 is provided with a mechanism for automatically acquiring observation data used for determination of failure diagnosis. The observation data includes, for example, drive current, vibration, differential sound, temperature of specific parts, timing when paper passes, etc. when a component (motor, solenoid, clutch, etc.) in the apparatus is operated alone. . At the time of failure diagnosis, the acquired observation data is compared with normal data measured in a normal state in advance. For example, it is determined whether or not there is a failure from the distribution of both, and prediction of future failure occurrence is performed.
For example, on the conveyance path 42, as a sensor member for collecting paper passage time information in the image forming apparatus 1, in the vicinity of the sheet feed roll pair 55 between the sheet feed roll pair 55 and the first conveyance roll pair 56. A first sensor (feedout sensor) 65 has a second sensor (feedout sensor) 66 in the vicinity of the first transport roll pair 56 between the paper feed roll pair 55 and the first transport roll pair 56, and a second transport roll. A third sensor (pre-registration sensor) 67 is provided in the vicinity of the second conveyance roll pair 57 between the pair 57 and the third conveyance roll pair 58, and a stop claw between the second conveyance roll pair 57 and the third conveyance roll pair 58. A fourth sensor (registate sensor) 68 is provided in the vicinity.
Although not shown, a fixing sensor and a discharge sensor are provided as sensor members for collecting paper passage time information in the image forming apparatus 1 on the conveyance path on the fixing roller 45 side, that is, the paper discharge processing side.
The pair of paper feed rolls 55 serves to prevent double feed (two or more paper feeds) in addition to guiding the paper to the first sensor 65, the second sensor 66, and the first transport roll pair 56. Also takes on the role of The first transport roll pair 56 and the second transport roll pair 57 serve to guide the paper to the photosensitive drum roll 32.
The register solenoid provided in the leading edge detection processing unit 44 is used to temporarily stop the sheet with a stop claw after a predetermined time has elapsed after the second sensor 66 is turned on. The purpose is to synchronize the timing for aligning the writing position in the sheet and the position of the image on the photosensitive drum roll 32.
Each sensor 65, 66, 67, 68 (hereinafter also referred to as a sheet timing sensor 69), which is a sensor member for collecting sheet passage time information, constitutes a sheet passage time detection unit that collects sheet conveyance passage time information. This is a paper detection member (paper timing sensor) that is installed to detect whether printing paper, which is an example of a transported body, is being transported at a predetermined timing. The detection signals obtained by the sensors are input to a measurement unit (not shown) that measures the conveyance timing and conveyance time (paper passage time) of the printing paper.
Each of the paper timing sensors 69 constituting the paper detection member can have various shapes and characteristics depending on the installation location. Basically, a device composed of a pair of light emitting elements (for example, light emitting diodes) and light receiving elements (for example, photodiodes or phototransistors) is used. A photo interrupter in which both the light emitting element and the light receiving element are integrated may be used.
Each paper timing sensor 69 may be either a transmission type (also called a blocking type) or a reflection type. Here, in the transmission type sensor, the light emitting element and the light receiving element are arranged to face each other, and in a state where the printing paper is not conveyed between them, the light receiving element receives the light of the light emitting element and is turned on. In a state where the printing paper passes between the two, the light from the light emitting element is blocked by the printing paper so that the printing paper is turned off.
On the other hand, the reflection type sensor is arranged so that the light from the light emitting element is reflected by the printing paper and the reflected light enters the light receiving element. When the printing paper is not conveyed, the light receiving element is turned off without receiving the light from the light emitting element. On the other hand, when the printing paper passes, the light from the light emitting element is reflected by the printing paper. It is turned on when incident. In the configuration of the present embodiment illustrated in FIG. 1, a reflection type photo interrupter is used for all the paper timing sensors 69.
A failure diagnosis unit (not shown; details will be described later) provided in the image forming apparatus 1 determines a component failure probability based on the collected transit time information. Then, the failure diagnosis unit determines the failure probability of the parts based on the passing time information collected using these paper timing sensors 69 and performs failure diagnosis. For example, there are observable nodes (passage time and standard deviation nodes) as result nodes in the state of the pick-up roll (nudger roll) 54 and the feed roll pair (feed roll) 55. The failure probability is determined by observing the average time and standard deviation of the sheet passing through the sensor, and when the value is larger than the reference, the failure probability is high.
Note that when the passage time of the printing paper is out of a predetermined time range from the start of conveyance of the printing paper, the image forming apparatus 1 determines that the printing cannot be performed normally and the sheet conveyance is performed at that time. Stop at that position. This is called normal jam (JAM).
Further, the image forming apparatus 1 includes a drive mechanism vibration detection unit 80 that detects vibration of each drive mechanism unit in the apparatus. The drive mechanism vibration detection unit 80 includes a vibration sensor 82 for detecting vibration in the apparatus for each block. As the vibration sensor 82, an acceleration sensor of a type that detects acceleration or an acoustic sensor of a type that detects sound generated from a machine can be used. In this example, a vibration sensor 82 is fixed to a main body chassis (not shown) immediately below the photosensitive drum roll 32. The position where the vibration sensor 82 is attached is not particularly limited. Any position within the image forming apparatus 1 that can detect all the accelerations and operating sounds of the drive mechanism sections of the respective blocks may be used, and the image forming apparatus 1 is not limited to the position immediately below the photosensitive drum roll 32.
Further, the image forming apparatus 1 includes a functional element that acquires environmental information related to the apparatus. As an example, first, the image forming apparatus 1 includes an operating temperature detection unit 84 that detects the temperature in the apparatus. In the present embodiment, the operating temperature detection unit 84 includes a temperature sensor 85, and uses the temperature sensor 85 to detect the temperature at a desired position in the apparatus.
As temperature sensor 85, it is preferable to use an electronic sensor composed of, for example, a platinum resistance thermometer, thermistor, thermocouple, or the like. Alternatively, a non-contact method such as a thermopile that measures infrared rays emitted from an object and measures the temperature of the object from the amount of the infrared rays may be used.
There are cases where the temperature rises due to a failure and heat is generated and the temperature inside the apparatus is abnormally high, and the temperature inside the apparatus rises because the ambient temperature where the apparatus is placed is high. The former sets, for example, whether the temperature control related to the fixing roller 45 is faulty, the circuit is faulty and abnormally generates heat, or the failure probability of each related component has a large dependency.
Regarding the fixing roller 45, since the operating temperature has a great influence on the image quality, a dedicated temperature sensor (not shown) is provided to monitor the operating temperature even at the component level. Here, the fixing roller 45 is illustrated as an example where it is necessary to monitor the operating temperature at the component level. However, the other members are not at the environmental level, but have a dedicated temperature sensor in the same manner as the fixing roller 45. The operating temperature may be monitored at the component level. In particular, it is preferable that components that affect image quality are monitored at the component level.
On the other hand, in the latter case, even if it is used within the specifications of the image forming apparatus, when it is left in such a situation for a long time, the roll deterioration accelerates, the friction coefficient between the roll and the paper changes, and the time change during sheet conveyance It leads to. Also in this case, the setting is made so that the failure probability of the related parts is increased.
The image forming apparatus 1 includes an operating humidity detection unit 86 that detects humidity in the apparatus as another example of a functional element that acquires environmental information in the apparatus. In the present embodiment, the operating humidity detection unit 86 includes a humidity sensor 87, and the humidity sensor 87 is used to detect the humidity at a desired position in the apparatus. As an example, a humidity sensor 87 may be disposed so as to detect the humidity in the vicinity of the paper in the vicinity of the paper feed tray 51. This is because the paper is greatly affected by humidity.
As the humidity sensor, for example, various sensors can be used such as a sensor that mainly uses a change in electrical properties due to adsorption / desorption of moisture in the atmosphere. For example, a wet and wet bulb type, a hair type, a quartz crystal type, a polymer type sensor, a metal oxide sensor or the like can be used. In particular, a polymer or a metal oxide is a small sensor having good compatibility with a circuit, and is preferable for application of this embodiment.
Humidity has been found to affect the roll and paper and the coefficient of friction between the paper. The higher the humidity, the greater the coefficient of friction between the sheets, making it difficult to carry and increasing the probability of misfeeds. Jam in the feed section. In addition, since the coefficient of friction between the paper and the roll varies even during the conveyance, the sheet conveyance time changes and the probability of occurrence of a jam increases.
Further, the image forming apparatus 1 includes a consumable material detection unit that is a functional element that detects the state of the consumable material used by the apparatus. In the present embodiment, as an example of the consumable material detection unit, first, a reflected light detection light sensor or a transmitted light detection light sensor is provided, and the thickness of the printing paper (pyeong A sheet information collecting unit 88 that detects sheet information such as a sheet type and a sheet type is provided in the vicinity of the sheet feeding tray 51 and at a predetermined position on the conveyance path 42.
If the paper is thicker (or thinner) than a specified value (for example, 50 to 100 g / m ^ 2; "^" indicates a power) or coated paper, the possibility of jamming increases, so paper information is detected. Used to calculate the failure probability.
As another example of the consumable material detection unit, the colorant cartridge 38 disposed in the vicinity of the developing device 34 is provided with a colorant remaining amount detection unit 89 that detects the remaining amount of toner (colorant). Yes. Since the monitoring mechanism of the remaining amount of the colorant is known to those skilled in the art, illustration and detailed description thereof will be omitted here.
In addition, a mechanism for monitoring the applied voltage supplied to the primary charger 33 is provided to monitor the state of the engine unit centered on the photosensitive drum roll 32 as observation data used for determination of failure diagnosis. Since this monitoring mechanism is known to those skilled in the art, illustration and detailed description thereof are omitted here.
<Overview of the operation of the image output unit>
In the image forming apparatus 1 configured as described above, when an image output unit is operated to form an image on a print sheet which is an example of a transported body, the sheet is transported from the sheet feed tray 51 by the sheet feed transport mechanism unit 50. The printing paper is fed out and conveyed to a predetermined position of the image output unit 30 to form an image on the printing paper.
For example, first, as the print process starts, the nudger solenoid operates to depress the pickup roll 54. At substantially the same time, a motor for rotating various rolls (pairs) in the image forming apparatus 1 starts rotating. The pickup roll 54 pushed down by the nudger solenoid comes into contact with the uppermost printing paper of the paper feeding tray 51 and guides one printing paper to the paper feeding roll pair 55.
The regigate solenoid temporarily stops the printing paper with a stop claw after a predetermined time has elapsed after the second sensor 66 is turned on. Thereafter, at a predetermined timing when the writing position in the printing paper and the position of the image on the photosensitive drum roll 32 are matched, the regigate solenoid releases the stop claw. As a result, the stop claw returns to its original position, and the third conveyance roll pair 58 feeds the printing paper between the photosensitive drum roll 32 and the transfer roll 35.
In the image output unit 30, first, a semiconductor laser 38 as a light source for forming a latent image is driven by image generation data from a host computer (not shown), thereby converting the image data into an optical signal. The irradiated laser beam L is irradiated toward the polygon mirror 39. The laser light L further scans the photosensitive drum roll 32 charged by the primary charger 33 via an optical system such as a reflection mirror 48, thereby forming an electrostatic latent image on the photosensitive drum roll 32. To do.
The electrostatic latent image is converted into a toner image (developed) by a developing device 34 to which a toner of a predetermined color (for example, black; black) is supplied. While passing between the drum roll 32 and the transfer roll 35, the image is transferred onto the printing paper by the transfer roll 35.
The toner and the latent image remaining on the photosensitive drum roll 32 are cleaned and erased by the cleaner roll 36 and a lamp (not shown). The developing roll 34 is provided with a developing clutch (not shown), and the developing timing is adjusted using this developing clutch.
The printing paper on which the toner has been transferred is heated and pressurized by the fixing roller 45, and the toner is fixed on the printing paper. Finally, the printing paper is discharged out of the machine through the discharge path.
<Configuration and Operation of Image Reading Unit>
The image reading unit 700 optically reads an image drawn on a sheet-like document to be read, and includes a platen cover 706. The image reading unit 700 has a platen glass (original platen) 712 that is slightly larger than the A3 size on which an original to be read is placed, and an optical system that includes a light receiving unit 742 that reads the original at a lower portion thereof. Or an image processing unit 760 on the image reading unit side.
The image reading unit 700 includes a light source 730 that emits light toward a surface (back surface) opposite to the document placement surface of the platen glass 712 below the platen glass 712 in the housing, and emits light from the light source 730. A full-rate carriage (F / F) having a substantially concave reflecting shade for reflecting the light to the platen glass 712, a reflecting mirror, a reflecting mirror for deflecting the reflected light from the platen glass 712 in a direction substantially parallel to the platen glass 712, etc. R-CRG) 734.
As the light source 730, a fluorescent lamp whose longitudinal direction is the main scanning direction (the direction orthogonal to the drawing in the drawing) is used. As the color of the illumination light emitted from the light source 730, a color matching the spectral optical characteristics of each line sensor constituting the light receiving unit 742 is used. For example, white light or green light is used.
The image reading unit 700 has two reflecting mirrors arranged so as to form a substantially right angle in the housing, and sequentially reflects the reflected light deflected by the full rate carriage 734 by about 90 ° (half-rate carriage ( H / R-CRG) 736. The full-rate carriage 734 and the half-rate carriage 736 are configured to be able to reciprocate in the sub-scanning direction (arrow X direction in FIG. 1) and the opposite direction in conjunction with a stepping motor (not shown).
Further, the image reading unit 700 includes a lens 740 that condenses the reflected light deflected by the reflection mirror of the half-rate carriage 736 at a predetermined focal position in the housing. The light receiving unit 742 receives the reflected light converged by the lens 740, reads an image in the main scanning direction (the depth direction in FIG. 1) substantially orthogonal to the sub-scanning direction, and outputs an image signal (analog electric) according to the density. Signal) in sequence.
The light receiving unit 742 is a line sensor (not shown) configured on a substrate 744 and configured by a photoelectric conversion element such as a photodiode, a CCD (Charge Coupled Device), a CMOS (Complementary Metal-oxide Semiconductor), and the like (details will be described later). ) Or a processing performed by a read signal processing unit including a drive control unit such as a CCD driver for driving a line sensor, which is also arranged on the substrate 744, and then sent to the image processing unit 760.
Although not shown, the image reading unit 700 also includes a wire, a driving pulley, and the like for moving the reading optical system and the light receiving unit 742 under the platen glass 712 in the housing. The driving pulley is reciprocally rotated by the driving force of the driving motor, and the wire is wound around the driving pulley by the rotation driving, so that the full-rate carriage 734 including the light source 730, the half-rate carriage 736 or the lower part of the platen glass 712 A reading optical system including the lens 740 is moved at a predetermined speed.
In the above configuration, the full-rate carriage 734 of the image reading unit 700 constituting the reading optical system is normally at a home position below the platen glass 712. Thus, the size of the document in the main scanning direction placed on the platen glass 712 by the line sensor included in the light receiving unit 742 without moving the light receiving unit 742 in the sub-scanning direction before starting reading. Can be detected.
On the other hand, at the time of a fixed reading method in which a document is placed on the platen glass 712 and read, the document is manually placed on the platen glass 712 as a document placing table, and the document is placed at an arbitrary position on the platen glass 712. In this state, the scanning optical system is moved at a constant speed in the direction of the arrow X with the fixed reading image destination position as a reference, and the original is exposed and the image is read. Analog captured image signals of red, green, and blue color components are obtained.
At the time of reading, a full rate including the light source 730 is applied so that illumination light from the light source 730 irradiates the entire surface of the document and the light receiving unit 742 reads the entire surface of the input image via the reading optical system such as the lens 740. The reading optical system including the carriage 734, the half-rate carriage 736, or the lens 740, the light receiving unit 742, and the like are relatively moved at a constant speed from left to right (sub-scanning direction) in FIG. It is done. That is, the image reading unit 700 reads a document image while moving the optical system at a constant speed.
The light receiving unit 742 sends the captured image signal of each spectral component obtained by capturing the original image with the line sensor to a read signal processing unit (not shown) provided on the substrate 744 in the same manner as the light receiving unit 742. The read signal processing unit performs desired analog signal processing on the captured image signal obtained by this reading, and then converts it into digital image data of each color component of red (R), green (G), and blue (B) The digital image data of red, green and blue is sent to the image processing unit 760.
<< Image inspection device >>
FIG. 2 is a schematic diagram showing an embodiment of an image inspection apparatus related to an output image reading method (ND170) and output image-related observation data information (ND180).
As shown in FIG. 2, the image inspection apparatus 5 has an image reading unit 100 that is an example of an image reading apparatus that reads an output image output from the image forming apparatus 1 as a document, and an image read by the image reading unit 100 has a defect. An image inspection processing unit 200 for inspecting whether there is an image, a data storage unit 300 for storing an image read by the image reading unit 100, a reference image used for inspection by the image inspection processing unit 200, or intermediate data being processed. I have.
The paper discharge tray 71 having a function as a document placement table on which an output image to be diagnosed is placed has a color that can distinguish between a document (printed paper) P and a base (here, the color of the paper discharge tray). attached.
The image reading unit 100 includes a camera head 102 that is an example of an imaging unit that reads an image of the document P. The camera head 102 includes a photographic lens 104, a CCD imaging device 106 configured as a line sensor or an area sensor, and a read signal processing unit 108. The camera head 102 is supported by an unillustrated support member so that the orientation of the photographic lens 104 with respect to P discharged onto the paper discharge tray 71 can be adjusted, and the whole image of the paper discharge tray 71 is photographed above the discharge port 582. It is installed so that it can be taken, and an image is taken obliquely from above to obtain a captured image.
FIG. 3 is a block diagram showing details of the image inspection apparatus 5 shown in FIG. The image inspection apparatus 5 of the present embodiment includes an image reading unit 100 that captures an image and acquires a captured image, an image inspection processing unit 200, a data storage unit 300, and a control unit 320 that controls each unit of the image inspection device 5. Is provided.
The data storage unit 300 holds a captured image acquired by the camera head 102, a processed image processed by the captured image processing unit 110, or various intermediate calculation results used for processing in the image inspection.
The image reading unit 100 includes a captured image processing unit 110 that performs predetermined processing on a captured image acquired by the camera head 102. The captured image processing unit 110 is subjected to shading correction by the shading correction unit 112 that corrects uneven illuminance (light quantity distribution characteristics of the optical system) and sensor pixel characteristic variation of the captured image read by the camera head 102, and the shading correction unit 112. An optical distortion correction unit 114 that corrects distortion (curvature) aberration mainly caused by the photographing lens 104 for the captured image. In addition, the captured image processing unit 110 refers to the document area specifying unit 116 that specifies the area of the document P in the captured image and the information about the document area specified by the document area specifying unit 116, and the optical distortion correcting unit 114 performs distortion. And a region cutout unit 118 that cuts out a necessary portion (region of the document P) from the captured image in which the aberration is corrected.
The shading correction performed by the shading correction unit 112 is a process for correcting uneven illuminance in the imaging region, characteristic variation of each pixel of the CCD image sensor 106, and the like. For example, the camera head 102 captures a blank sheet in advance and stores it in the data storage unit 300 as the white reference DW [i, j]. i and j represent pixel positions. Next, the shading correction unit 112 corrects the captured image D [i, j] based on the equation (1).
Here, “n” is a bit resolution after correction, and if 8-bit resolution, n = 8, and takes a value of “0 to 255” gradation. Here, the method of holding the white reference data for all the pixels has been described. For example, for simplicity, a method of setting the peak value of the entire captured image as the white reference DW, or the peak value DW [j] for each line. A method using white as a reference is applicable.
The optical distortion correction unit 114 corrects the aberration caused by the photographing lens 104 as follows. For example, if the aberration at the incident angle θ to the photographing lens 104 is d, the distance from the photographing lens 104 to the imaging surface of the CCD image sensor 106 is c, and the distance from the optical axis of the imaging position on the imaging surface is r. Aberration d is expressed by the following equation (2). Therefore, the optical distortion correction unit 114 corrects distortion based on this characteristic. Since aberration d is generally proportional to the cube of r, distortion constant can be corrected by obtaining a proportionality constant based on lens characteristics.
The document area specifying unit 116 extracts a document area from the captured image every time an image is captured by the camera head 102. The area extraction unit 118 refers to the document area information extracted every time shooting is performed, and extracts the portion of the document P in the captured image as a captured image range to be compared with the reference image.
Although it is considered that the location and orientation of the original P to be discharged to the paper discharge tray 71 are fixed to some extent, it may be possible to fix the cutout portion. Or paper is tilted. In this case, if the cutout portion is fixed, the original portion cannot be cut out accurately. If a portion that is not a document is cut out as a captured image range to be compared, that portion may be erroneously detected as a defect. Therefore, as in the present embodiment, the actual document P portion is specified every time it is photographed, and the specified portion is cut out, so that the document portion can be cut out with high accuracy. Therefore, it becomes possible to detect defects with high accuracy.
For example, the applicant of the present application proposed in Japanese Patent Application No. 2001-334163 as a mechanism for performing error correction for further reducing the error between the captured image read by the camera head 102 and the reference image for inspection. A correction coefficient calculation unit and an image correction processing unit may be provided. For details of the correction coefficient calculation unit and the image correction processing unit, refer to Japanese Patent Application No. 2001-334163.
For example, the image correction processing unit applies an enhancement process (sharpness process) to the captured image acquired by the camera head 102 by applying an edge enhancement filter or the like generally used in image processing, or the captured image is This is an example of a noise reduction unit that reduces a noise component that has noise removal processing that applies a smoothing filter or the like to remove moire or smoothes halftone data to reduce the noise component of a captured image (Smoothing process) is performed.
The correction coefficient calculation unit calculates a blur characteristic correction coefficient for correcting the location dependence of the blur characteristic caused by the tilt scan of the captured image acquired by the camera head. For example, a line-by-line correction coefficient for enhancement processing and noise removal processing is calculated from a trapezoidal document area, and the calculated correction coefficient is set in the image correction processing unit.
When performing the enhancement process or the smoothing process, the image correction processing unit adjusts the parameters of the respective filters with the correction coefficient calculated by the correction coefficient calculation unit. In this case, it is more preferable to change both the sharpness and the degree of noise removal for each line, that is, it is preferable to perform the enhancement process and the noise removal process simultaneously for each line.
Similar to the configuration proposed by the applicant of this application in Japanese Patent Application No. 2001-334163, the image inspection processing unit 200 uses a reference image (original image data) stored in the data storage unit 300 as a reference for image inspection. A reference image processing unit 210 that reduces the error in comparing both images by performing resolution conversion and blurring processing (smoothing processing) in the same manner as the captured image acquired by the head 102; And an image inspection unit 230 that inspects the quality of the captured image based on the reference image output from the processing unit 210.
The reference image processing unit 210 refers to the document region information specified by the document region specifying unit 116 and converts the reference image read from the data storage unit 300 into the same shape as the document region (perspective conversion) (perspective conversion) ( (Also referred to as a perspective conversion unit) 212, a resolution conversion unit 214 that performs resolution conversion processing on the perspective-converted image using linear interpolation or the like, and a high-frequency component for the reference image that has been subjected to resolution conversion processing. And a fringe processing unit 216 that performs a fading process to be reduced.
In the present embodiment, since the camera head 102 photographs the document P from obliquely above, the rectangular document P is deformed into a trapezoidal shape with respect to the captured image area on the photographed image data. The geometric conversion unit 212 converts a rectangular reference image so as to match the trapezoidal shape of the captured image.
The blur processing unit 216 is based on the variance σ of the Gaussian function (that is, the Gaussian filter) so that the reference image subjected to resolution conversion by the resolution conversion unit 214 has the same degree of blurring of the captured image. To adjust (generally reduce) the sharpness of the reference image for inspection. In short, the blur caused by the optical system of the camera head 102 in the captured image data is reflected. At this time, a correction coefficient for Gaussian filter processing is calculated by referring to the trapezoidal shape of the document area specified by the document area specifying unit 116, and based on this, the trapezoidal reference image after the perspective transformation is used. Processing (Gaussian filter processing) is performed. Note that the Gaussian filter processing to which the variance σ is applied is a known technique as shown in, for example, Japanese Patent Laid-Open No. 5-225388, and therefore detailed description thereof is omitted.
FIG. 4 is a flowchart showing a processing procedure in the image inspection apparatus 5 having the above configuration. In the image inspection apparatus 5 according to the above-described embodiment, when the printer unit 500 is in a standby state, the camera head 102 periodically takes an image on the paper discharge tray 71 according to an instruction from the control unit 320 and outputs the image.
The control unit 320 compares the image on the paper discharge tray 71 stored in the data storage unit 300, which is an example of the reference image, with the captured image acquired by the camera head 102, and the document is on the paper discharge tray 71. If the document is on the paper discharge tray 71, the image inspection flag is turned off and the standby state is continued. If there is no document on the paper discharge tray 71, the image can be inspected. The flag is turned on and the standby state is continued (S100).
When the control unit 320 receives a print job in the standby state (S102), the control unit 320 determines an image inspection possible flag (S104). When the flag indicating that image inspection is possible is off (S104-NO), the control unit 320 causes the image output unit 30 to perform normal printing processing (S106), and returns to the standby state (S180). On the other hand, when the flag indicating that image inspection is possible is on (S104-YES), the control unit 320 performs image inspection on the Nth sheet (first of course, first sheet) of the print job as follows.
That is, the control unit 320 first causes the image output unit 30 to start print processing for the Nth sheet (naturally, the first sheet at first) (S108), and the image output unit 30 outputs the developed image data. The data is sent to the unit 572 and stored in the data storage unit 300. When the rendering processing of the last image data is completed, a signal for completion of the first image processing from the print output processing unit 572 is waited (S130), and when the completion signal comes, a shooting start trigger signal is sent to the camera head 102 after a specified time. (S132).
The camera head 102 releases the shutter (photographs) in synchronization with the trigger signal, but immediately after the document P (print output paper) is output onto the paper discharge tray 71 at this time. The camera head 102 stores a trapezoidal captured image obtained by shooting a rectangular document in the data storage unit 300.
When there are a plurality of print jobs (N sheets), the control unit 320 advances the remaining print processes in parallel with the processes (S130 to S162) described later until all the processes for the plurality of sheets (N sheets) are completed. (S110). These processes are shown as parallel processes in the figure, but can also be executed as time-division sequential processes. When the plurality of sheets (N sheets) are all completed, the controller 320 returns to the standby state (S112). In this way, all captured images captured by the camera head 102 are converted into digital signals and stored in the data storage unit 300.
The captured image processing unit 110 performs preprocessing on the captured image captured by the camera head 102. That is, the shading correction unit 112 reads a captured image taken by the camera head 102 from the data storage unit 300, and performs shading correction to correct illuminance unevenness and sensor pixel characteristic variation (S134). The optical distortion correction unit 114 performs optical distortion correction for correcting the distortion aberration of the photographing lens 104 on the image subjected to the shading correction, and stores processed image data in the data storage unit 300. (S136).
The document area specifying unit 116 uses a difference between the color of the background paper discharge tray 184 and the white of the edge of the document P, edge detection, and the like for the captured image subjected to shading correction and optical distortion correction. The coordinate points A, B, C, and D for specifying the document area are extracted, and information on the extracted coordinate values of the four points is notified to the area extracting unit 118 and stored in the data storage unit 300 (S138).
Next, the control unit 320 determines whether or not to perform perspective transformation (perspective transformation) processing on the captured image that has been subjected to shading correction and optical distortion correction by the captured image processing unit 110 (S140). In the present invention, since the perspective transformation process is performed on the reference image without performing the perspective transformation process on the captured image, the perspective transformation process (S142) on the captured image is passed (S140-NO).
When performing the perspective transformation process on the captured image (S140-YES), the control unit 320 instructs the geometric transformation unit provided on the captured image processing unit 110 side not illustrated in FIG. 3 to perform the perspective transformation process. To do. Upon receiving this instruction, the geometric conversion unit reads the captured image that has been subjected to the shading correction from the data storage unit 300, extracts a trapezoidal document area from the captured image by edge detection or the like, and corrects the trapezoid hypotenuse before the perspective conversion process. The coefficient is calculated, and the trapezoidal document area is actually perspective-transformed into a rectangle (S142).
The area cutout unit 118 cuts out a document area in the captured image captured by the camera head 102 as a captured image range to be inspected (S144). In this embodiment, the area cutout unit 118 cuts out a trapezoidal area surrounded by the coordinate values of the four vertices extracted by the document area specifying unit 116 as a captured image range to be inspected, and the cut out trapezoidal shape. The image data indicating the image of the original area is stored in the data storage unit 300.
When the perspective conversion process is performed on the captured image, the document area specifying unit 116 converts the coordinate values of the coordinate points A, B, C, and D of the document area returned to the rectangular shape subjected to the perspective conversion process. Is notified to the area extracting unit 118 and stored in the data storage unit 300 (S138). The area cutout unit 118 captures an area to be inspected in an area surrounded by the coordinate values of the four vertices extracted by the document area specifying unit 116 from the captured image captured by the camera head 102 and subjected to the perspective transformation process. The image range is cut out (S144).
Next, the geometric conversion unit 212 of the reference image processing unit 210 includes image data indicating the original image that has been printed as a reference image, and information indicating the coordinate values of the four vertices of the captured image extracted by the document area specifying unit 116. Are read from the data storage unit 300. First, a correction coefficient necessary for the perspective transformation process is calculated based on the coordinate values of the four vertices of the captured image, and the calculated correction coefficient is applied so as to match the trapezoidal shape of the captured image with respect to the reference image. A perspective transformation process is performed on the (S150).
Next, the resolution conversion unit 214 converts the resolution of the reference image so that the size of the trapezoidal reference image is equal to the size of the trapezoidal captured image to be inspected (S152).
Next, the blurring processing unit 216 is extracted by the document area specifying unit 116 in order to reflect the blur caused by the optical system of the camera head 102 in the captured image data with respect to the perspective-transformed reference image. Referring to the vertex coordinates A, B, C, and D indicating the trapezoidal shape, a correction coefficient value gx corresponding to the trapezoidal shape of the image used in the Gaussian filter processing is calculated, and the trapezoid after perspective transformation is based on this The reference image is subjected to a blurring process (Gaussian filter process), and the processed image is stored in the data storage unit 300 (S154).
Here, the reference image is subjected to the blurring process because the reference image has an ideal tone characteristic, and therefore, by performing a blurring process equivalent to the blurring characteristic of the captured image, This is because the blurring process can be performed corresponding to the blur distribution and the error in the difference process described below can be minimized.
Next, the alignment processing unit 232 compares the reference image that has been subjected to the blurring processing unit 216 of the reference image processing unit 210 and the captured image that has been preprocessed by the image reading unit 100. The alignment process is started (S156). For example, the alignment processing unit 232 sequentially calculates the normalized cross-correlation coefficient while scanning the captured image using the reference image as a template, and regards the place where the correlation coefficient is the highest as the position where the position is aligned. Align with template matching.
Next, the defect abnormality detection processing unit 234 detects an image quality defect by comparing the read image with the reference image, and a defect state analysis function that analyzes the state of the defect detected by the image quality defect detection function. And. For example, the defect abnormality detection processing unit 234 performs the pre-processing in the captured image processing unit 110 and the captured image (trapezoidal shape) to be inspected stored in the data storage unit 300, and the reference image processing unit 210. Difference processing is performed to obtain a difference from the reference image (trapezoidal shape) that has been subjected to the perspective transformation processing and the blurring processing (compare both) (S160). And the defect abnormality detection process part 234 detects the presence or absence of the defect and abnormality of a captured image based on the difference result obtained by the difference process (S162). The alignment processing unit 232 and the defect abnormality detection processing unit 234 perform the above processing on the entire surface of the reference image, and return to the standby state after completion.
For example, when the normalized cross-correlation coefficient at the position where the position is matched is lower than the determination index value, the defect abnormality detection processing unit 234 performs the defect abnormality detection processing by regarding that the image defect exists at that portion. Thereby, for example, if there is a defect pattern such as a black spot in a print output, that is, a captured image, the normalized cross-correlation coefficient clearly decreases at a place including the defect, so a black spot remains in the difference result.
Therefore, for example, by setting an appropriate density threshold value and binarizing the difference result, a black spot can be specified, and the presence or absence of an image defect can be easily determined. Not only the detection of local image defects such as black spots, but also the characteristics of defects in the output image used for image diagnosis, including the detection of image quality defects such as the overall density unevenness and density shift of the captured image. The output image-related observation data information (ND180) as an index to be expressed can be applied to detection of the shape, size, density, contour state, orientation, position, periodicity, occurrence region, etc. of the defect.
When the defect abnormality detection processing unit 234 detects an image defect or an image quality defect, the image inspection apparatus 5 automatically activates a failure occurrence location specifying process for the failure diagnosis apparatus. Alternatively, instead of starting automatically, a warning is issued on a user interface (not shown) such as a predetermined display device or an audio signal generator (for example, a speaker), or print management software or the like is used to communicate through a communication network. A warning is issued to a client terminal (for example, a personal computer) that is going to print, and further, a printer maintenance company is notified through a communication network via a remote maintenance system. After that, after an instruction is given to the failure diagnosis apparatus, the failure location specifying process is started.
As described above, in the image inspection apparatus 5 of the above embodiment, the captured image is converted into an image viewed from the front (that is, converted back to a rectangular image) and compared with the reference image that is the original image. However, both of the images that are not rectangular (the trapezoid in the previous example) are subjected to a perspective transformation process on the reference image so as to match the shape of the captured image (generally trapezoidal) due to the imaging state. Compared. The perspective conversion process for the reference image does not require an interpolation process for obtaining pixel values of more pixel points from a small number of pixel points, and thus there is no problem that the reliability of data after the perspective conversion is lowered. Therefore, the presence / absence of an image defect can be inspected with higher accuracy.
If this concept is followed, processing for optical distortion should not correct the captured image, but the reference image should be distorted to match the shape of the captured image. The amount of change in image information due to distortion correction is much smaller, and the influence on defect detection accuracy is extremely small. In addition, at the time of image defect inspection, comparing images without distortion is much simpler than comparing images with optical distortion. Therefore, as shown in the above-described embodiment, performing the optical distortion correction on the captured image leads to an improvement in the performance of the entire image defect inspection system.
In addition, for each imaging, the vertex coordinates of the document area are extracted to identify the document area, the correction coefficient for the perspective transformation process for the reference image is calculated based on the information of the specified document area, and the calculated correction coefficient is applied. By performing the perspective conversion process on the reference image, it is possible to perform a perspective conversion process in accordance with the actual imaging state on the reference image. The accuracy at the time of comparison / inspection with the reference image is improved, and it becomes possible to inspect image defects and image quality abnormalities with better inspection accuracy.
In addition, by performing a blurring process on the reference image based on the specified document area information, the blurring process according to the distribution depending on the location of the blurring degree generated during the tilt scan is performed in the actual imaging state. Thus, a comparison image having a blur characteristic close to that of the captured image can be generated.
As described above, if the perspective conversion process and the blur correction process are performed on the reference image side with almost no image processing applied to the captured image, errors in the difference process in the defect abnormality detection processing unit 234 are performed. Will be much less and more accurate inspection will be possible.
Note that the mechanism of the image inspection apparatus 5 described above is that the print image processed by the image output unit 30 is output onto the paper discharge tray 71 using an area sensor or line sensor in the vicinity of the paper discharge tray 71. However, any method for reading the image to be inspected may be used, and a line sensor is used in the process of transporting the paper on which the image is formed. Even if an automatic reading method (ND174) or a manual reading method (ND176) through user operation is adopted, the image defect inspection process is not affected. However, in the case of a reading method that reads from an oblique direction, processing related to distortion correction is not necessary.
<< Failure diagnosis function in image forming apparatus >>
<Outline of fault diagnosis function>
FIG. 5 and FIG. 6 are diagrams for explaining an overview of functions in a failure diagnosis apparatus provided in the image forming apparatus 1.
For example, when performing a fault diagnosis that automatically specifies a fault location of an image quality defect system or a fault diagnosis that automatically specifies a fault location of a mechanical system (paper transport system) (S10), the diagnosis architecture is used. As a result, the normal state data is acquired before the failure occurs, data such as the operating state of the device and environmental conditions (collectively referred to as actual data) is acquired, and the inference engine uses these information. Diagnosis is performed with reference to the calculated failure probability (S13). The failure diagnosis here means not only the determination of the presence or absence of a failure but also a prediction diagnosis for predicting the occurrence of a future failure.
Note that the inference engine and the functional part that performs failure diagnosis are not limited to the configuration built in the main body of the image forming apparatus 1, but may be provided in, for example, a management center connected to the image forming apparatus 1 via a network (S14). In this case, normal data and actual data are sent to the management center via the network, and diagnosis is performed at the management center. Alternatively, only the inference engine may be placed at the management center, and only the failure probability may be calculated at the management center. Further, regarding the diagnosis result, for example, a form that a customer engineer (CE; Customers Engineer) confirms at the management center may be adopted, or by sending the diagnosis result to the image forming apparatus 1, the image forming apparatus 1 side You may take the form which a customer engineer or a customer (customer) confirms.
Here, in the present embodiment, a Bayesian network is used as an inference engine for calculating the failure probability (S11, S15). Fault diagnosis using a Bayesian network uses a probabilistic model genetic algorithm that generates search points using statistical information of good individuals in the population, and determines the dependency between nodes (variables). It is an optimization approach that estimates distribution using stochastic graph structures (called Bayesian networks or causal networks).
As will be described in detail later, when performing a failure diagnosis of the image forming apparatus 1 using a Bayesian network, for example, a component having a state indicating whether or not the component has caused a failure as a component of the Bayesian network A network configuration having a state node and a plurality of information nodes connected to the component state node and causally related to the component state is set (S16).
For example, when performing a fault diagnosis that automatically identifies a fault location of an image quality defect system, a node representing an image quality defect status, a node representing a component status, a history information node, a node representing environmental information, a user A Bayesian network including nodes representing operations is configured (S11). Then, after image formation is performed under predetermined operating conditions, the output image is read by a predetermined reading device, and the presence or absence of an image result is determined.
Here, when an image quality defect is detected, the diagnosis mode is entered, the defect state is determined, the failure probability is calculated by the Bayesian network together with the component state, machine history, and environmental information, and the calculated failure probability is calculated. Based on this, a candidate for a failure location is extracted. At this time, if it is difficult to narrow down the number of failure location candidates to one, the result information by the user operation is further added, the failure probability is recalculated, and the failure candidates are narrowed down from the result (S12).
Also, when performing failure diagnosis that automatically identifies the failure location of the mechanical system (paper transport system), a Bayesian network is constructed that includes nodes that represent component states, history information, and nodes that represent environmental information. Then, when the paper transport timing is observed and an abnormality is detected, the failure diagnosis mode is entered, operation data of each component is collected, and the failure probability is calculated by the Bayesian network together with the history and environmental information of the device. A failure location candidate is extracted based on the calculated failure probability.
For this reason, in the failure diagnosis apparatus, as information (variable; node) for determining the failure probability of a part, for example, observation data information (ND120), history information (ND130), environment information (ND140), and consumption of the transportation system The material information (ND150) or the specification information (ND160) of the image forming apparatus 1 is directly acquired by using data collection application software.
Further, as information unique to the present embodiment that enables fault diagnosis relating to image quality defects, a method for reading an output image formed by the image forming apparatus 1 (ND170), observation data information related to an output image (ND180), or Observation data information (ND190) of the image transfer system is also directly acquired by using data collection application software. Furthermore, in this embodiment, user operation information (ND196) is also taken in as necessary.
The observation data information (ND120) of the carrier system corresponds to the component observation information node ND2 of the Bayesian network, and gives an apparatus operating state variable. Specifically, the driving of a motor, a solenoid or the like acquired using a sensor Acquired based on information (component information) such as current, vibration and time indicating the operating state of the member. In the present embodiment, the component information is automatically acquired by providing various sensors in the image forming apparatus 1.
For example, the current (ND122) that passes through the sheet conveyance path based on the sheet conveyance time information acquired by using the sheet timing sensor 69, the current indicating the operation state of the components (parts), particularly the driving members such as the motor, solenoid, and plunger. Information, vibration data (ND124) acquired using an acceleration sensor used as the vibration sensor 82 of the drive mechanism vibration detection unit 80, or an acoustic sensor used as the vibration sensor 82 of the drive mechanism vibration detection unit 80 There is an operating sound (ND126) acquired. For example, probability data can be determined based on the acquired operating sound.
The current information indicating the operation state of the drive member is acquired by operating the components (motor, solenoid, clutch) in the apparatus alone and detecting the drive current at that time.
The history information (ND 130) corresponds to the history information node ND3 of the Bayesian network, represents the usage status of the image forming apparatus 1, and has a function of reinforcing component information that gives an apparatus operating state variable. In the present embodiment, the history information is automatically acquired by providing various sensors in the image forming apparatus 1 and storing the acquired information in a storage medium.
For example, the number of print sheets fed from the paper feed tray 51 to the conveyance path 42, that is, the number of paper feeds (ND132) corresponding to the cumulative number of prints, the number of years of use of the device (machine) after the device installation (ND134), and the unit period Usage frequency (ND135) determined by the number of paper feeds in the component, component replacement information and usage history (ND136), failure replacement frequency information (ND138), and a non-volatile storage medium (semiconductor memory, hard disk, etc.) provided in the image forming apparatus ) Stores log data (ND139) such as parts replacement history and usage history.
For example, the number of paper feeds directly affects roll wear, gear wear, or motor bearing wear, and affects component conditions. Therefore, information (paper feed number; ND132) indicating how much paper the feed unit has sent after the apparatus is installed at a predetermined location or after component replacement is managed for each component. It is effective to perform failure diagnosis.
In addition to component information, data that takes into account component history, device history (usage status), device installation environment, etc. is automatically obtained to calculate the probability, and then incorporated into the Bayesian network. Thus, no prior knowledge is required, and accurate fault diagnosis can be performed with a simple operation. Even a service person with no prior knowledge or little experience can perform an accurate failure diagnosis with a simple operation.
As the sheet feed number (ND132), the number of feeds from when the image forming apparatus 1 is newly installed, when consumables are replaced, or when parts are replaced is used. This number of paper feeds affects the life of all moving parts (motors, solenoids, clutches, rolls, gears, belts, etc.) due to wear and the like. As a threshold value that is a standard for deterioration of each component such as a motor and a solenoid, for example, the probability is set to 500000 sheets or more (this value varies depending on the type and type of component). This value is recorded on the non-volatile storage medium provided inside the image forming apparatus by sequentially recording the cumulative number of paper feeds used by the customer since new installation, replacement of consumables, or replacement of parts. As the observation information, the probability is determined at the time of diagnosis.
The number of paper feeds for a component is reset to zero when the component replacement or the like occurs. Therefore, the cumulative number of feeds of all parts at the time of new installation is zero, but the number of feeds stored in the non-volatile storage medium differs for each part depending on the maintenance status of the image forming apparatus. .
The environmental information (ND140) corresponds to the environmental information node ND4 of the Bayesian network, represents the operating environment of the image forming apparatus 1, and is an ambient environmental condition that affects the state of the component. For example, temperature data (ND142) acquired using the temperature sensor 85 of the operating temperature detector 84, humidity data (ND144) acquired using the humidity sensor 87 of the operating humidity detector 86, temperature data, There is an installation position (address, building, or latitude / longitude / altitude, etc.) (ND146) of the image forming apparatus 1 that affects the humidity data.
The temperature and humidity affect the coefficient of friction with the printing paper of the feed roll 55a that constitutes the pair of paper feed rolls 55 and the coefficient of friction between sheets, and affect the sheet conveyance time. Therefore, temperature data and humidity data are used. It is effective to perform fault diagnosis. Whether or not the device is used in a location suitable for the device specifications affects the failure, so it is effective to perform a failure diagnosis using the installation position.
The consumable material information (ND150) corresponds to the consumable material information node ND5 of the Bayesian network, and the print paper thickness (paper thickness information) and paper type (ND152) acquired by the consumable material detection unit 88, or This is information on the consumables used by the apparatus, such as the type of colorant, the type of dye / pigment, and the remaining amount (ND154).
The specification information (ND160) corresponds to the specification information node ND6 of the Bayesian network, and is information for specifying whether or not it is different from a normal device. For example, according to the destination (ND162) or customer request. Special parts are used (ND164).
The output image reading method (ND170) is unique for diagnosing an image defect failure, and represents a method of reading an output image used for image diagnosis. For example, a method of automatically reading a print image processed by the image output unit 30 in the vicinity of the discharge tray 71 when it is output on the discharge tray 71 using an area sensor or a line sensor is employed. (ND172, ND174).
Alternatively, a method of automatically reading with a line sensor in a process in which a sheet on which an image is formed is conveyed toward the sheet discharge tray 71 outside the apparatus (ND174). In order to adopt such a configuration, it is preferable to provide a line sensor 616 having a length corresponding to the paper width between the fixing roller 45 and the paper discharge tray 71 in the configuration shown in FIG.
Alternatively, using the image reading unit 700 of the image forming apparatus 1 as a reading device, or using another scanner as a reading device, the user sets the output paper in these reading devices and reads it manually. An approach can also be taken (ND176).
The output image-related observation data information (ND180) is unique for diagnosing an image defect failure, and is an index (hereinafter also referred to as an image defect feature value) representing a defect feature of an output image used for image diagnosis. ). For example, the shape, size, density, contour state, orientation, position, periodicity, and generation area of the black defect. Here, the shape of the defect is a dot, a black line, a black band, a white blank, a white band, density unevenness, a full white, a full black, or the like. The size of the defect is the size for a point, and the thickness / length for a line / band.
The state of the outline relates to sharpness, and is sharp (high sharpness) or blurred (low sharpness). The direction of the defect includes a paper feed direction, a main scanning direction, and random. The position of the defect is the same position, the position corresponding to the part, random, etc. each time. The periodicity of the defect is whether or not the defect appears repeatedly on the paper. The defect occurrence area indicates where the defect appears on the sheet, and is, for example, whether it is a border area of the sheet or the back side of the sheet.
The observation data information (ND190) of the image transfer system is information indicating the operation state of components related to image formation, and excludes signals indicating the operation state of drive members related to the transport system. For example, the temperature of the fixing roller 45 whose temperature is monitored at the component level, the applied voltage supplied to the primary charger 33, the patch density detected using the pattern detection unit 614, and the remaining amount of colorant For example, the remaining amount of toner (coloring material) detected using the unit 89 is used. The observation data information (ND 190) of these image transfer systems is stored in a nonvolatile storage medium (for example, the storage medium 532) as setting information necessary for appropriate image formation.
The user operation information (ND196) is when the failure location information presented by performing the failure diagnosis automatically using the failure diagnosis device is not appropriate (for example, the failure location candidates are not narrowed down to one). In addition, information input for reprocessing is performed to cause the image forming apparatus 1 to perform similar processing under different conditions. It includes not only the reprocessing instruction but also information on the changed operating conditions.
Upon receiving a user instruction, the image forming apparatus 1 sets the operation conditions different from the previous operation conditions, and similarly, the observation data information (ND120) of the transport system, the observation data information (ND180) related to the output image, or the image. Additional test result information such as transcription system observation data information (ND190) is acquired.
<< Configuration example of failure diagnosis device >>
FIG. 7 is a block diagram illustrating a configuration example of a failure diagnosis apparatus provided in the image forming apparatus 1.
The failure diagnosis device 3 is configured to obtain the information shown in FIG. 6 and perform failure diagnosis. For example, the failure diagnosis apparatus 3 monitors the usage status of the component state information acquisition unit 12 that acquires component information indicating the operation state of the component as observation data information and the image forming apparatus 1, and stores the monitoring result in a nonvolatile storage. And a history information acquisition management unit 13 that manages history information by registering and holding in a medium.
In addition, the failure diagnosis apparatus 3 acquires, as environment information, ambient environment conditions that affect the state of components such as temperature and humidity based on information detected by the operating temperature detector 84 and the operating humidity detector 86. Based on information detected by the information acquisition unit 14 and the consumable material detection unit 88, information on the consumable materials used by the apparatus such as the thickness of the printing paper, the paper type, or the color type, type, and remaining amount of the colorant is acquired. A consumable material information acquisition unit 15 that performs the operation, and a specification information acquisition unit 16 that acquires the specification information of the image forming apparatus 1.
Further, the failure diagnosis apparatus 3 includes an image inspection apparatus 5 including the image reading unit 100 and the image inspection processing unit 200 described above, and an additional operation information acquisition unit as a specific configuration for diagnosing an image defect failure. 19. The image reading unit 100 reads a print image output from the image forming apparatus. The image inspection processing unit 200 detects an image quality defect by comparing the image read by the image reading unit 100 with a reference image, and analyzes the state of the detected defect.
Further, the failure diagnosis apparatus 3 includes a feature amount extraction unit 502, a reference feature amount storage unit 530 that stores a reference feature amount serving as a determination index at the time of failure diagnosis in a predetermined storage medium (preferably a non-volatile semiconductor memory) 532, The failure determination unit 540 that performs failure determination and failure prediction, the inference engine (failure probability inference unit) 560 that infers the failure probability used in failure determination and failure prediction of the failure determination unit 540, or the failure determination result and inspection contents And a failure diagnosis unit 500 having a notification unit 570 and the like for notification to the inside.
The feature amount extraction unit 502 is based on the operation state signal indicating the operation state while the driving member of the transport system acquired by the component state information acquisition unit 12 is operating for a predetermined period, and the feature amount of the operation state signal Ask for. The feature amount acquisition unit 502 acquires not only the part state information acquisition unit 12 but also information from the history information acquisition management unit 13, the environment information acquisition unit 14, the consumable material information acquisition unit 15, or the specification information acquisition unit 16. Then, the feature amount of the acquired information is also obtained. The feature quantity extraction unit 502 has a function of an operation state signal receiving unit that receives an operation state signal from the component state information acquisition unit 12 and other information from the history information acquisition management unit 13.
Although not shown, in addition to the storage medium 532, the reference feature amount storage unit 530 reads the reference feature amount from the storage medium 532 and a writing control unit for writing the reference feature amount into the storage medium 532. A read control unit is provided.
The storage medium 532 has a function of a history storage unit that holds history information of various operation state signals acquired by the feature amount extraction unit 502 in the image forming apparatus 1.
As the reference feature amount, for example, a mechanical member (including a driving member such as a motor or a solenoid) that constitutes a driving mechanism unit of the transport system or an electric member (a driving signal generation unit 150 or a driving circuit) that drives the mechanical member is normal. The feature amount acquired by the feature amount extraction unit 502 in a normal state in which the feature amount is operating is used. Alternatively, instead of the feature amount obtained by the feature amount extraction unit 502, an operating current or rated value of vibration of a stepping motor or the like in the image forming apparatus 1 may be used.
Further, when a failure is detected, the feature amount acquired by the feature amount extraction unit 502 when each component member fails is used as a reference feature amount for determining the failure location or failure state. The reference feature amount related to the failure state may be detected by the feature amount extraction unit 502 by forcing each member of the device to be in a failure state, or based on maintenance information collected in a management center or the like. The acquired information may be used. The image forming apparatus 1 and the management center may be connected to the network, and the information at the time of failure stored in the storage medium 532 may be updated periodically.
In addition, the failure determination unit 540 compares the reference feature amount stored in the storage medium 532 with the actual feature amount that is the feature amount obtained by the feature amount extraction unit 502 at the time of failure diagnosis, so that the failure determination unit 540 Diagnosis processing related to the failure is performed, such as whether or not a failure has occurred and the possibility of a failure occurring in the future.
For example, when performing a failure diagnosis that automatically identifies a failure location of an image quality defect system, the failure diagnosis unit 500 detects defect status information and image formation analyzed by the defect abnormality detection processing unit 234 of the image inspection processing unit 200. The failure probability of the location that causes the image quality defect using the status information of the components constituting the device 1, the history information of the device, the surrounding environment information where the device is installed, and the follow-up test result information obtained by user operation Inference is performed by the inference engine 560, and a failure location candidate is extracted by the failure determination unit 540 based on the failure probability calculated by the inference engine 560.
The failure determination unit 540 has a function of a failure candidate extraction unit that uses the inference engine 560 to narrow down failure candidates, and narrows down failure candidates, failure determination results (failure presence / absence, failure location, failure content), failure The notification unit 570 is notified of a prediction result (presence / absence of possibility of failure, failure location, failure content), inspection content, acquired operation state signal, and the like.
Here, when the automatic determination process is performed, if the number of failure point candidates cannot be narrowed down to one, the inference engine waits for the input of the additional test result information obtained under different operating conditions obtained by the user operation. At 560, the failure probability is recalculated, and a more appropriate failure location is extracted based on the failure probability acquired under each operation condition.
For example, the notification unit 570 uses the failure determination result received from the failure determination unit 540 as a customer (operator or owner of the image forming apparatus 1) and a customer engineer who maintains the image forming apparatus 1 (maintenance, maintenance, management). Or a customer engineer such as a management center that manages the image forming apparatus 1 or a customer.
For example, when notifying the customer directly, it is possible to notify the image forming apparatus 1 with an alarm such as a display panel or a speaker. The customer can see or hear it and inform the service center of the failure location and the content of the failure. In addition, when directly informing the customer engineer who maintains the image forming apparatus 1, a failure occurs using a public telephone line, a PDA (Personal Digital Assistant), a mobile phone, or a mobile terminal such as a PHS (Personal Handy-phone System). Etc. can be contacted. It is also possible to send failure location and failure content data to terminals owned by customer engineers.
Further, when notifying the management center or the like that manages the image forming apparatus 1, a public telephone line or a portable terminal can be used as in the case of notifying the customer engineer directly. You can also contact us via the Internet. In these cases as well, failure location and failure content data can be sent to the terminal of the management center.
Further, the image forming apparatus 1 (failure state specifying unit 248) does not specify the failure location or the failure content, and the inspection content of the failure diagnosis performed by the failure diagnosis unit 500 and the data such as the operation state signal used there are stored. The management center may be notified, and the management center may narrow down failure candidates or specify the failure location and the failure content.
<< Configuration using an electronic computer >>
FIG. 8 shows a case where the fault diagnosis apparatus 3 is configured by software using a CPU and a memory, that is, the fault diagnosis apparatus 3 is realized by software using the function of a computer (electronic computer) such as a personal computer. It is a block diagram which shows an example of a hardware configuration.
A program suitable for realizing a failure diagnosis method and a failure diagnosis apparatus according to the present invention to which a Bayesian network method described later is applied by software using a computer (computer) or a computer-readable storage medium storing the program Can also be extracted as an invention.
Of course, the failure diagnosis apparatus 3 and the failure diagnosis unit 500 are not limited to such a configuration using a computer, but are configured by a combination of dedicated hardware that functions as each function unit shown in FIGS. You can also. By adopting a mechanism for executing processing by software, it is possible to enjoy the advantage that the processing procedure and the like can be easily changed without changing hardware.
When making a computer execute a fault diagnosis function using a series of Bayesian network processing by software, a computer (such as an embedded microcomputer) in which a program constituting the software is incorporated in dedicated hardware, or A system on a chip (SOC) that implements a desired system by mounting functions such as a CPU (Central Processing Unit), logic circuit, and storage device on one chip, or various programs are installed. Thus, it is installed from a recording medium in a general-purpose personal computer or the like that can execute various functions.
The recording medium causes a change state of energy such as magnetism, light, electricity, etc. to the reading device provided in the hardware resource of the computer according to the description content of the program, and in the form of a signal corresponding thereto. The program description can be transmitted to the reader.
For example, a magnetic disk (including a flexible disk FD), an optical disk (CD-ROM (Compact Disc-Read Only Memory)), a DVD on which a program is recorded, which is distributed to provide a program to a customer separately from a computer. (Including Digital Versatile Disc), magneto-optical disc (including MD (Mini Disc)), or package media (portable storage media) made of semiconductor memory, etc. The program may be composed of a ROM or a hard disk device that is provided to the customer in a state where the program is recorded. Or the program which comprises software may be provided via communication networks, such as a wire communication or radio | wireless.
For example, a storage medium in which a program code of software that realizes a function of calculating (including updating) a failure probability in Bayesian network processing is supplied to a system or apparatus, and a computer (or CPU or MPU) of the system or apparatus By reading and executing the program code stored in the storage medium, the same effect as that achieved by hardware can be achieved. In this case, the program code itself read from the storage medium realizes a failure diagnosis processing function using a Bayesian network.
Further, by executing the program code read by the computer, not only a function of performing a Bayesian network is realized, but also an OS (operating system; basic software) running on the computer based on the instruction of the program code May perform part or all of the actual processing, and the processing may realize a failure probability calculation processing function, a failure location determination processing function, or the like.
Further, after the program code read from the storage medium is written in a memory provided in a function expansion card inserted into the computer or a function expansion unit connected to the computer, the function expansion is performed based on the instruction of the program code. The CPU or the like provided in the card or the function expansion unit may perform part or all of the actual processing, and the failure probability calculation processing function or the failure location determination processing function may be realized by the processing.
In the case where the failure diagnosis apparatus 3 is incorporated in the image forming apparatus 1 having a copying function, the electronic computer shown in FIG. 10 includes, for example, a copying application, a printer application, a facsimile (FAX) application, or another application. Software similar to that in a conventional image forming apparatus (multifunction machine) such as a processing program is incorporated. A control program for transmitting and receiving data to and from the outside via the network 9 is also incorporated.
At this time, the program is provided as a file describing a program code for realizing the failure probability calculation processing function and the failure location determination processing function using the Bayesian network. In this case, the program is provided as a batch program file. However, the program may be provided as an individual program module according to the hardware configuration of the system configured by a computer. For example, it may be provided as add-in software incorporated in existing copying apparatus control software or printer control software (printer driver).
For example, the computer system 900 constituting the failure diagnosis apparatus 3 includes a controller unit 901, a predetermined storage medium such as a hard disk device, a flexible disk (FD) drive, a CD-ROM (Compact Disk ROM) drive, a semiconductor memory controller, or the like. And a recording / reading control unit 902 for reading and recording data from.
The controller unit 901 includes a CPU (Central Processing Unit) 912, a ROM (Read Only Memory) 913 which is a read-only storage unit, and a RAM (Random Access) which can be written and read at any time and is an example of a volatile storage unit. Memory) 915 and RAM (described as NVRAM) 916 which is an example of a nonvolatile storage unit. The NVRAM 916 stores failure probability information of each part weighted by, for example, usage time, frequency, number of copies / prints, and the like.
In the above description, the “volatile storage unit” means a storage unit in a form in which the stored contents disappear when the power of the failure diagnosis apparatus 3 is turned off. On the other hand, the “non-volatile storage unit” means a storage unit in a form that keeps stored contents even when the main power supply of the failure diagnosis apparatus 3 is turned off. Any memory device can be used as long as it can retain the stored contents. The semiconductor memory device itself is not limited to a nonvolatile memory device, and a backup power supply is provided to make a volatile memory device “nonvolatile”. You may comprise as follows. Further, the present invention is not limited to a semiconductor memory element, and may be configured using a medium such as a magnetic disk or an optical disk.
The computer system 900 also includes an instruction input unit 903 having a keyboard, a mouse, and the like as a function unit that forms a customer interface, and a display output unit 904 that presents predetermined information such as a guidance screen and a processing result during operation to the customer. , And an interface unit (IF unit) 909 that performs an interface function with each functional unit. When the failure diagnosis apparatus 3 is incorporated and integrated into the image forming apparatus 1 having a copying function, an image reading unit (scanner unit) 905 that reads an image to be processed and a processed image as a predetermined output medium (for example, An image forming unit 906 for outputting to (printing paper) is also provided.
Examples of the interface unit 909 include a system bus 991 that is a transfer path of processing data (including image data) and control data, a scanner IF unit 995 that functions as an interface with the image reading unit 905, an image forming unit 906, and the like. It has a printer IF unit 996 that functions as an interface with other printers, and a communication IF unit 999 that mediates transfer of communication data with the network 9 such as the Internet.
The display device 904 includes, for example, a display control unit 942 and a display unit 944 made up of a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display). For example, the display control unit 942 displays guidance information, the entire image captured by the image reading unit 905, and the like on the display unit 944. It is also used as a display device for notifying customers of failure determination results and inspection details. Note that by using the display unit 944 having the touch panel 932 on the display surface, the instruction input unit 903 for inputting predetermined information with a fingertip or a pen can be configured.
The image reading unit 905 has a function of an image input terminal. For example, by using a full-width array of a CCD solid-state imaging device, the image reading unit 905 reads an image on the document by irradiating light to the document sent to the reading position. The red R, green G, and blue B analog video signals representing the read image are converted into digital signals.
The image forming unit 906 uses, for example, an electrophotographic type, thermal type, thermal transfer type, ink jet type, or similar conventional image forming process to display an image represented by the image signal obtained by the image reading unit 905. A visible image is formed (printed) on plain paper or thermal paper.
For this reason, the image forming unit 906 uses the image processing unit 962 that generates print output data such as binary signals of yellow Y, magenta M, cyan C, and black K, for example, and the failure diagnosis apparatus 3 as a digital printing system. A raster output scan-based print engine 964 is provided for operation.
In such a configuration, the CPU 912 controls the entire system via the system bus 991. The ROM 913 stores a control program for the CPU 912 and the like. The RAM 915 is configured by SRAM (Static Random Access Memory) or the like, and stores program control variables, data for various processes, and the like. Further, the RAM 915 stores an electronic document (not only character data but also image data) acquired by a predetermined application program, image data acquired by the image reading unit 905 provided in the apparatus, and externally. An area for temporarily storing acquired electronic data and the like is included.
For example, a program that causes a computer to execute a failure probability calculation processing function and a failure diagnosis processing function using a Bayesian network is distributed through a recording medium such as a CD-ROM. Alternatively, this program may be stored in the FD instead of the CD-ROM. In addition, an MO drive may be provided to store the program in the MO, or the program may be stored in another recording medium such as a nonvolatile semiconductor memory card such as a flash memory. Furthermore, the program may be downloaded and acquired from another server or the like via the network 9 such as the Internet, or may be updated.
As a recording medium for providing the program, in addition to FD and CD-ROM, an optical recording medium such as DVD, a magnetic recording medium such as MD, a magneto-optical recording medium such as PD, a tape medium, and a magnetic medium. A semiconductor memory such as a recording medium, an IC card, or a miniature card can be used. Some or all of the FD, CD-ROM, and the like as an example of a recording medium when realizing a failure probability calculation processing function and a failure diagnosis processing function using a Bayesian network in the failure diagnosis apparatus 3 described later Can store functions.
The hard disk device also includes an area for storing data for various processes by the control program and temporarily storing a large amount of image data acquired by the image reading unit 905, print data acquired from the outside, and the like. It is out. The hard disk device, FD drive, or CD-ROM drive is used, for example, for registering program data for causing the CPU 912 to execute processing such as content acquisition, address acquisition, or address setting by software. .
Instead of performing all processing of each functional part of the failure diagnosis apparatus 3 by software, a processing circuit 908 that performs part of these functional parts by dedicated hardware may be provided. Although the mechanism performed by software can flexibly cope with parallel processing and continuous processing, the processing time becomes longer as the processing becomes complicated, so that a reduction in processing speed becomes a problem. On the other hand, it is possible to construct an accelerator system with a higher speed by using a hardware processing circuit. Even if the processing is complicated, the accelerator system can prevent the processing speed from being lowered, and high throughput can be obtained.
In the case of the failure diagnosis apparatus 3 of the present embodiment applied to the image forming apparatus 1, the processing circuit 908 includes observation data such as paper passage time, drive current, vibration, operating sound, or light quantity shown in FIG. Sensor system data acquisition function for acquiring information (ND120), environmental information such as temperature and humidity (ND140), output image related observation data information (ND180), image transfer system observation data information (ND190), etc. This corresponds to the part 908a.
<< Details of fault diagnosis using Bayesian network >>
FIG. 9 is a Bayesian network model diagram illustrating a basic configuration example of a Bayesian network used at the time of failure diagnosis in the failure diagnosis unit 500.
A Bayesian network is a directed acyclic graph that represents a causal relationship between variables. When a parent is given, a conditional probability distribution is associated with the variable. Bayesian networks model problem areas using probability theory. Given information about others, a Bayesian network representation of the problem is used to provide information about a subset of variables.
A Bayesian network consists of a set of variables (nodes; indicated by ellipses) and a set of arcs (indicated by arrows) that indicate directed edges (connections between variables). Arrows called arcs indicate causal relationships and are linked from cause to result in the direction of the arrow.
Each node (variable) has a set of mutually exclusive states. In each node, a probability (conditional probability table) that a result is generated from a cause is set in advance. A node forms a directed acyclic graph (DAG) with a directed edge. A conditional probability table P (v | w1,..., Wn) is defined for each variable v having parents w1,. If v has no parent, this table is reduced to the marginal probability P (v).
A node that does not have an incoming arrow is set by the occurrence probability of that node. A major feature of the Bayesian network is that it is possible to infer probabilities from information that can be directly observed (or obtained), such as the presence or absence of a failure, and to calculate the probability of a state that cannot be observed directly (whether it is a failure). .
In other words, since the Bayesian network represents a problem area where the causal relationship is complex, the causal relationship between a plurality of variables is sequentially connected and expressed as a network having a graph structure. In a Bayesian network, the dependency between design variables is expressed by a directed graph. And the intuitive meaning of the arrow from node X to node Y, where the set of variables forms a node of the network, the set of links or arrows connects the node pairs, and X directly affects Y (Y Is dependent on X) and is presented as a graph structure. The graph is a directed acyclic graph that does not have a cycle in the direction of the arrow.
In the figure, the nodes indicated by hatching are nodes that can be directly observed. By calculating the probability of a node shown without hatching, the state of the component (possibility of failure) can be determined. For example, Bayes' theorem is used for the probability calculation of each node. However, in a network configuration in which there are many nodes and a loop is formed, the calculation is practically impossible due to the enormous amount of calculation. Therefore, various efficient calculation algorithms for accurately updating the probability in the Bayesian network have been devised, and calculation software is also sold by several manufacturers. For example, refer to http://staff.aist.go.jp/y.motomura/bn2002/presen/motomura-tut.files/frame.htm; online.
The Bayesian network shown in FIG. 9 is shown so as to correspond to FIG. 6, that is, for example, as shown in FIG. 9, the Bayesian network of this embodiment represents the environmental state centering on the component state node ND1. A component observation information node (environment node) ND2, a history information node ND3, an environment information node ND4, a consumable material information node ND5, and a specification information node ND6 are configured based on the causal relationship.
The component state node ND1 is a node representing a component state, and the probability of this part is calculated to determine whether or not a failure has occurred. In each node, a probability table summarizing probability data representing the strength of the causal relationship is placed in advance. As the initial value of the probability data, past data or MTBF (Mean Time Between Failure) of parts can be used. If the value is too small, a relative value between components that makes the magnitude relationship of the failure probability clear may be used.
The observation information node ND2 is automatically acquired by using a sensor member in the apparatus, such as a component observation information node, an observation data node related to an output image, or an observation data node of an image transfer system, which is used for determination of failure diagnosis. Information. Here, the component observation information node is sheet conveyance time information, drive current information, vibration information, or the like in this embodiment. The observation data node related to the output image is information such as the shape, size, density, contour, orientation, position, periodicity, and generation area of the defect. The observation data node of the image transfer system is information such as component temperature, applied voltage, patch density, or remaining amount of colorant (for example, toner).
The history information node ND3 represents the usage status of the image forming apparatus 1, and in this embodiment, history information on the number of feeds is used. As described with reference to FIG. 6, the number of feeds is information indicating how much paper has been fed by the feed unit after the image forming apparatus 1 is installed at the treatment position or after component replacement. It directly affects the wear of gears and the wear of motor bearings, and affects the state of components.
The environment information node ND4 is an ambient environment condition that affects the state of the component, and in this embodiment, is the temperature and humidity. As described with reference to FIG. 6, the temperature and humidity affect the coefficient of friction with the paper of the feed roll and the coefficient of friction between the sheets, and affect the sheet conveyance time.
The consumable material information node ND5 is information on consumable materials used by the apparatus, such as paper thickness, paper type, colorant color type, colorant type, or remaining amount. For example, the friction between the roll and the paper and the friction between the papers differ depending on the paper type and the paper thickness, and the influence of the ambient temperature and humidity also differs. Similarly, if an appropriate colorant is not used, image formation is affected. In other words, the consumable specification affects the failure diagnosis.
The specification information node ND6 is information such as a destination and special parts. For example, devices suitable for cold regions and coastal areas use members suitable for cold regions and coasts. When performing a failure diagnosis of a device that uses a component according to the destination, even if the failure rate calculated for that component is the same, it is determined without considering the original component specifications. I cannot make accurate judgments. That is, the product specification and the component specification based on the product specification affect the failure diagnosis.
The user operation node (ND9) is information that causes the image forming apparatus 1 to perform the same processing while changing the operating condition, and includes information about the operating condition after the change.
FIG. 10 is a more specific configuration example of the Bayesian network, and shows a configuration example of the Bayesian network in the case of performing a failure diagnosis that identifies the failure location of the image quality defect system. As shown in the figure, this Bayesian network includes a component status node ND1 representing status information of components constituting the image forming apparatus, a history information node ND3 representing history information of the apparatus, and peripheral environment information where the apparatus is installed. It includes an environment information node ND4 that represents, an observation state node ND8 that represents state information of image quality defects, and a user operation node ND9 that represents additional test result information obtained by user operation.
Each node is connected so as to have a relationship of “cause” → “result”. For example, the relationship between the “cause node” and the “observation state node ND2” is such that “observation state (light concentration, streaks / bands, etc.)” appears based on “cause”. On the other hand, the relationship between “history information node ND3” and “cause node” is that “cause” (part deterioration, etc.) occurs based on “state based on history information (many copies, long operation years, etc.)”. A relationship is established.
<< Processing procedure of overall operation of fault diagnosis device >>
FIG. 11 is a flowchart showing an overall outline of a failure diagnosis processing procedure in relation to an image defect using a Bayesian network in the failure diagnosis unit shown in FIG. FIG. 12 is a chart showing a processing procedure for specifying a failure location using a Bayesian network at the time of occurrence of an image defect at a block diagram level.
When the image forming apparatus 1 forms an image on printing paper and discharges it outside the apparatus, the image reading unit 100 of the image inspection apparatus 5 reads the output image (S600, S702). As described above, it may be configured to automatically read when output onto the paper discharge tray 71 using an area sensor or line sensor in the vicinity of the paper discharge tray 71, as shown in FIG. In the configuration, the image may be read using a line sensor 616 having a length corresponding to the sheet width provided between the fixing roller 45 and the paper discharge tray 71, or the image reading unit 700 of the image forming apparatus 1 or another scanner may be used. A configuration may be employed in which the user uses the reading sheet and manually sets the output sheet in these reading apparatuses.
The defect abnormality detection processing unit 234 of the image inspection processing unit 200 detects the image quality defect by comparing the read image with the reference image (S602). When the defect abnormality detection processing unit 234 finds an image defect (S606-YES, S704), the feature amount extraction unit 502 processes the image data and analyzes the state of the detected defect (S706).
For example, the feature amount extraction unit 502 specifies a black spot by setting an appropriate density threshold value and binarizing the difference result. Alternatively, feature quantities representing the state of image defects such as overall density unevenness, density shift, defect shape, size, density, contour state, orientation, position, periodicity, and generation area are extracted (S608). .
<Fault location identification processing>
Next, the failure diagnosis unit 500 activates a failure location identification process using a Bayesian network (S720). For example, the failure determination unit 540 activates the operation state information acquisition process, and acquires the operation state feature amount in cooperation with the feature amount extraction unit 502 (S740). For example, a drum motor that drives a photosensitive drum roll 32 that is a driving member of an image transfer system, a solenoid, a clutch, and the like are driven in this order, and each is operated as a diagnostic target driving member alone. The failure determination unit 540 waits for the data indicating the operation state such as current and vibration to be taken in by the feature amount extraction unit 502 (S728).
When the drive unit side of the image transfer system detects a drive trigger signal (S744), it collects operation state data such as current and vibration for each drive member in the inspection target block in a single operation state (S746). The obtained operation state data is passed to the feature amount extraction unit 502.
The feature amount extraction unit 502 performs data processing according to the equation (3) based on the collected operation state data, obtains the feature amount Vn of the actual working state necessary for failure determination, and uses this as the actual feature amount Vf as the failure determination unit. Pass to 540. Note that the collected raw operation state data such as current and vibration may be passed to the failure determination unit 540 as the actual feature amount Vf.
In addition, the feature quantity extraction unit 502 is not limited to information on driving members, but is also related to temperature of specific parts, status information related to other parts, environmental data such as temperature and humidity in the apparatus in the operating state, history information, Various data necessary for failure diagnosis using other Bayesian networks, such as device specifications, are also acquired (S610).
When the failure determination unit 540 receives data (such as a defect state analysis result, a feature value Vn in the working state, or raw operation state data) from the feature amount extraction unit 502, the failure determination unit 540 cooperates with the inference engine 560 to create the Bayesian network Utilizing this, a failure location candidate (failure cause candidate) is extracted (S612), and the result is notified to the notification unit 270.
For example, the failure determination unit 540 passes the data received from the feature amount extraction unit 502 (defect state analysis result, actual state feature amount Vn, or raw operation state data) to the inference engine 560. The inference engine 560 calculates a failure probability based on the data, that is, starts a Bayesian network calculation process (S730).
Based on the calculated failure probability, the inference engine 560 extracts a failure location candidate (failure cause candidate) causing a failure related to image quality (S732), and notifies the failure state specifying unit 248 of the result. At this time, the inference engine 560 adjusts the failure probability to be calculated in consideration of the distribution of various measurement data indicated by actual feature values (average value, median value, standard deviation, variance σ, etc.). Similarly, even in a normal state, the failure probability to be calculated is adjusted in consideration of the distribution of various measurement data indicated by the reference feature amount. Based on how much the failure probability based on the operation state signal measured in the actual operation state deviates from the normal range, or whether there is an abnormal output that should not exist, etc. To do.
For example, even if the average value and median value (median) of the measurement data are the same, if the variation is large, the failure probability is set higher. This is effective for determining a failure mode having characteristics such as variations in image density and sharpness that increase greatly at the time of failure.
Further, if the variations are approximately the same, the failure probability is made the same when the average value is within a certain range. In the case of measurement data having characteristics that fluctuate in the numerical value itself even if there is no failure, erroneous determination can be avoided without widening the allowable range of determination. Since it is not necessary to widen the allowable range of determination, there is no deterioration in determination accuracy.
The failure state identification unit 248 notifies the customer of the failure location candidate extracted by the inference engine 560 via the notification unit 270 using a multimedia device such as a display device or an audio device (S616, S734). . If the failure location candidates can be narrowed down to one, the process is completed (S618—YES).
However, it is not always possible to extract one failure cause candidate by such automatic determination processing. As described above, when the number of failure location candidates cannot be narrowed down to one (S618-NO), the process waits for a reprocessing instruction with the operating condition changed (S620-NO). When the user confirms that the failure location candidate has not been narrowed down to one by notification of the failure diagnosis result via the notification unit 270, the user changes the operation condition and instructs re-output and diagnosis processing.
Upon receiving a user instruction corresponding to the notification of the failure diagnosis result via the notification unit 270, the feature quantity extraction unit 502 of the failure diagnosis unit 500 sets the operation condition different from the previous operation condition, and similarly, the transport system Additional test result information such as observation data information (ND120), output image related observation data information (ND180) or image transfer system observation data information (ND190) (S622-YES), and the inference engine 560 receives the additional test results. The extracted feature amount of the information is passed (S624).
Thereafter, the inference engine 560 and the failure determination unit 540 cooperate to calculate the failure probability based on the information after the change of the operating condition in the same manner as described above, and based on the calculated failure probability, the failure location candidate ( Failure cause candidates) are extracted (S732), and the result is notified to the failure state identifying unit 248. At this time, the result information by the user operation is added, the failure probability is recalculated, and failure candidates are narrowed down from the result.
If the failure candidate cannot be narrowed down to one, if there is no special instruction from the user within a predetermined time or if an instruction to stop the process is received, the process is completed without obtaining the additional test result information. (S622-NO).
<Specific examples of failure diagnosis methods>
FIG. 13 illustrates a specific example of the operation of the failure diagnosis apparatus 3 having the above-described configuration, and is an example of a Bayesian network when a black line is generated in the configuration example of the image defect failure diagnosis. As shown in the figure, the nodes are connected so as to have a relationship of “cause” → “result”. For example, the relationship between “drum scratches” and “line width information” is such that “line width information” such as the occurrence of a thin line based on “drum scratches” appears.
On the other hand, the relationship between “feed number history information” and “fuser” is based on the state based on “number of feeds” (number of feeds or more), and the possibility of black line generation due to “fuser” deterioration increases. It holds.
The initial value of the probability data of each node is determined based on, for example, past data. Thereafter, the probability of each node may be periodically updated based on market trouble statistical data such as the replacement frequency of parts and the frequency of occurrence of defects.
The processing procedure shown in FIGS. 11 and 12 is an example of failure diagnosis by automatically collecting internal state information, history information, peripheral environment information, etc. of the apparatus based on image defect detection and its feature amount. This is effective in determining whether the cause of the failure when the black line is generated is “scratches on the photosensitive drum roll 32” or “deterioration of the fixing roller pair (fuser) 74”. . It is also effective when identifying the cause of black line failure based on where the black line occurs.
For example, black line generation is caused not only on the image output unit side, such as “scratches on the photoconductive drum roll 32” or “deterioration of the fixing roller 45”, but also on the image reading unit side such as scratches on the platen glass. In some cases, it may be difficult to isolate the cause of failure by using only automatic diagnosis.
In such a case, according to the processing procedure shown in FIGS. 11 and 12, for example, after receiving the result of changing the output condition such as changing the orientation of the document or the orientation of the printing paper, the additional information by the customer operation is acquired. Then, by recalculating the failure occurrence probability and determining the location dependency (that is, block dependency) of the black line occurrence, the failure occurs as if it is the image reading unit side or the image output unit side It will be possible to carve out the points.
Of course, as described above, not only the mechanical system, such as the drive mechanism unit system and the image reading unit side / image output unit side, but also the image processing (pure electric) system or the mechanical system can be distinguished. .
Also, for example, output test patterns built into each board in order. By acquiring information as to which test pattern of the substrate the black line was generated as additional information by the customer operation, it becomes possible to narrow down the location where the failure occurred.
As described above, according to the failure diagnosis processing of the failure diagnosis apparatus 3 described in the present embodiment, when a defect is detected in an image formed under a predetermined operation condition, the operation state under the operation condition is detected. The failure probability is calculated by the Bayesian network based on the component status, machine history, environmental information, etc., and the failure location candidates are extracted based on the calculated failure probability, but the failure location candidates can be narrowed down to one. When there was not, the result information by user operation was further added, the failure probability was recalculated, and failure candidates were narrowed down from the result.
It has a means to automatically collect component (member) information using sensors, etc., in the process of identifying the fault location that caused the image abnormality. From the collected information and the information Since the device automatically determines the failure probability of a part using the Bayesian network based on the extracted feature value (information related to distribution in the previous example), the failure cause location is specified. At this time, no prior knowledge or experience is required, and accurate, homogeneous, and rapid failure diagnosis that does not depend on the competence of the person performing maintenance is possible. It is possible to construct a user-friendly mechanism that does not vary as in the case of user input, does not require user input action, and does not give stress to the user.
In addition, even if it is difficult to isolate the cause of failure by automatic diagnosis alone, additional information by user operation is input and the probability of failure occurrence is recalculated, enabling even less experienced service personnel to perform more accurate diagnosis with simple operation. Become.
In addition to component (member) information, internal state information such as device temperature and humidity, history information, and surrounding environment information are also automatically collected, and a Bayesian network is created based on the features. Since the failure probability of the part is determined and the failed part is specified, more accurate failure diagnosis can be performed.
For example, by incorporating a program for automatically collecting various data necessary for troubleshooting (here, failure diagnosis) as an automatic troubleshooting mechanism, the speed of diagnosis can be increased. Since it is not necessary to interactively collect data from customers, it is possible to realize a simple diagnostic system that does not bother customers.
In addition, since the inspection result is notified to the customer, it is possible to promptly notify the customer and to greatly reduce downtime.
As described above, according to the failure diagnosis device 3 of the present embodiment, it is not necessary to have prior knowledge, experience, or maintenance of various members, various failure states, or the possibility of failure. Accurate, homogeneous, and rapid fault diagnosis has become possible.
FIG. 14 is a diagram illustrating a configuration example of a failure diagnosis system in which failure diagnosis is performed in a management center connected to the image forming apparatus 1 via a communication line.
In the illustrated failure diagnosis system 800, first, a plurality of image forming apparatuses 1 configured to operate by software processing using a memory such as a CPU 912, a RAM 915, or an NVRAM 916 as shown in FIG. (A, B,..., Z) are connected to a local area network (LAN) 802. The local network 802 is further connected to an external communication network 806 such as the Internet via a communication line 804.
Each of the image forming apparatuses 1 (A, B,..., Z) includes observation data information (ND120) such as paper passage time, drive current, vibration, operation sound, or light quantity shown in FIG. A sensor system data acquisition function unit 908a is provided for acquiring environmental information (ND140), output image-related observation data information (ND180), image transfer system observation data information (ND190), and the like. It is configured to be able to notify the outside via the communication IF 999 and the local network 802.
The management center 810 is provided with a host computer 812, and performs communication processing with the image forming apparatus 1 (A, B,... Z) via the external communication network 806, the communication line 804, and the local network 802. Is possible.
The host computer 812 has data processing functions such as a feature quantity acquisition function part related to failure diagnosis, a failure determination function part, and an inference engine function part, excluding the data acquisition function part 908a in the failure diagnosis apparatus 3 described in the above embodiment. An application program for realizing the part by software processing is installed. For example, the feature amount acquisition function part as the data receiving function unit is the feature amount acquisition unit 502, the paper passage time feature amount acquisition unit 220, the control unit 250, and the like illustrated in FIG. The data processing function part includes, for example, the failure determination unit 540, the inference engine 560, the notification unit 570, and the like illustrated in FIG.
With such a configuration, the failure diagnosis system 800 is provided with a failure diagnosis unit including a failure determination unit 540 and an inference engine 560 in the management center 810 outside the apparatus using a communication line such as the Internet or a local network 802. The image forming apparatus 1 is configured to perform failure diagnosis using the host computer 812 of the management center 810.
Even with such a system configuration, as a means for automatically collecting operation state information of components (members) related to image formation using a sensor or the like in the process of identifying the fault location that caused the image abnormality. A data acquisition function unit 908a is provided, and measurement data collected by the data acquisition function unit 908a can be sent to the management center 810 via a communication network.
Therefore, in the management center 810, the host computer 812 automatically determines the failure probability of the part using the Bayesian network based on the measurement data and the feature amount extracted from the measurement data, and selects the failed part. Can be identified. Further, when failure point candidates cannot be narrowed down to one, it is possible to further receive the result information from the user operation, recalculate the failure probability, and narrow down the failure candidates from the result.
Therefore, similar to the above-described embodiment described in the form in which the failure diagnosis apparatus 3 is provided in the image forming apparatus 1, no prior knowledge or experience is required for failure diagnosis, and accurate and homogeneous that does not depend on the ability of the person performing maintenance. Quick failure diagnosis becomes possible. In addition to component (member) information, internal state information such as device temperature and humidity, history information, and surrounding environment information are also automatically collected, and based on the features, Bayesian networks are used. Since the failure probability of the part can be determined and the failed part can be specified, more accurate failure diagnosis can be performed.
At the management center, failure diagnosis for a plurality of image forming apparatuses can be performed collectively via the Internet or the like, so the service person can see the diagnosis results in advance and prepare for going to repair. It is possible to quickly respond to parts to be used.
In addition, since a preliminary diagnosis (an example of a failure prediction process) can be performed before failure occurs, planned maintenance can be performed before failure occurs, and service costs can be significantly reduced. This point is the same in the case where the failure diagnosis apparatus 3 is provided in the image forming apparatus 1, but it is more drastically that a diagnosis can be made and a maintenance plan can be made without going to the installation place of the apparatus. Service costs can be reduced.
For example, in FIGS. 11 and 12, when an image defect occurs, it is difficult to isolate the cause of the failure only by automatic diagnosis when performing a failure diagnosis that identifies a failure location of the image quality defect system using a Bayesian network. In the example described above, additional information by user operation is input to recalculate the failure occurrence probability based on information obtained by changing the operation condition. However, the application range is not limited to such a case.
For example, even in failure diagnosis that identifies a failure location in a mechanical system (paper transport system), if it is difficult to isolate the cause of failure by only automatic diagnosis, additional information by user operation is input, for example, operation is performed at a higher speed than usual. Or, conversely, by operating again at low speed, re-calculating the failure occurrence probability based on the information that changed the operating conditions, and more accurate failure for those where the failure state appears remarkably due to different operation speed Diagnosis becomes possible.
In the above embodiment, the failure diagnosis apparatus is applied to an image forming apparatus such as a copying machine, a printer function, a facsimile function, or a multifunction machine having a combination of these functions. However, the failure diagnosis apparatus is applied. The apparatus is not limited to the image forming apparatus, and may be applied to other arbitrary devices such as home appliances and automobiles.
1 is a diagram illustrating a configuration example of an image forming apparatus equipped with an embodiment of a failure diagnosis apparatus according to the present invention. It is the schematic which shows one Embodiment of an image inspection apparatus. It is a block diagram which shows the detail of the image inspection apparatus shown in FIG. It is a flowchart which shows the process sequence in the image inspection apparatus 5 of the said structure. FIG. 3 is a diagram (part 1) for explaining an overview of functions in a failure diagnosis apparatus provided in the image forming apparatus. FIG. 6 is a diagram (part 2) for explaining an overview of functions in a failure diagnosis apparatus provided in the image forming apparatus. 1 is a block diagram illustrating a configuration example of a failure diagnosis apparatus provided in an image forming apparatus. It is a block diagram which shows an example of a hardware configuration in the case of implement | achieving a failure diagnosis apparatus like software using an electronic computer. It is a Bayesian network model figure which shows the basic structural example of the Bayesian network utilized at the time of a failure diagnosis in a failure diagnostic part. It is a figure which shows the structural example of a Bayesian network in the case of performing the failure diagnosis which pinpoints the failure location of an image quality defect type | system | group. It is a flowchart which shows the whole outline | summary of the process sequence of the failure diagnosis in relation to the image defect using a Bayesian network in the failure diagnosis part shown in FIG. It is the chart which represented the process sequence which specifies a failure location using a Bayesian network at the time of image defect generation on the block diagram level. It is the figure which showed an example of the Bayesian network at the time of black line generation | occurrence | production. 1 is a diagram illustrating a configuration example of a failure diagnosis system configured to perform failure diagnosis at a management center connected to an image forming apparatus via a communication line.
DESCRIPTION OF SYMBOLS 1 ... Image forming apparatus, 3 ... Failure diagnosis apparatus, 5 ... Image inspection apparatus, 12 ... Component state information acquisition part, 13 ... History information acquisition management part, 14 ... Environmental information acquisition part, 15 ... Consumable material information acquisition part, 16 ... Specification information acquisition unit, 19 ... Additional operation information acquisition unit, 30 ... Image output unit, 80 ... Drive mechanism vibration detection unit, 84 ... Operating temperature detection unit, 86 ... Operating humidity detection unit, 88 ... Paper information collection unit, 89 ... Colorant remaining amount detection unit, 100 ... Image reading unit, 200 ... Image defect detection unit, 500 ... Failure diagnosis unit, 502 ... Feature quantity acquisition unit (operation state signal receiving unit), 530 ... Reference feature quantity storage unit, 532 ... storage medium (history storage unit), 540 ... failure determination unit, 560 ... inference engine, 570 ... notification unit
A failure diagnosis method for diagnosing a failure that occurs in a diagnosis target device including a plurality of locations that may cause a failure,
Obtaining an operation state signal indicating an operation state while the diagnosis target device is operating under the first operation condition;
When a failure occurs , based on the acquired operation state signal, by analyzing the cause of the failure of the device by modeling and analyzing each component member constituting the diagnosis target device,
The diagnostic target device is operated under a second operating condition different from the first operating condition, and indicates an operating state while the diagnostic target device is operating under the second operating condition. Get the operating state signal,
Based on the acquired operation state signal, the cause of the device failure is modeled and analyzed using a Bayesian network model, which is an analysis model for performing failure diagnosis based on the failure probability, thereby configuring the diagnosis target device A failure diagnosis method characterized by narrowing down failure candidates to a single one from a plurality of locations that are included in the diagnosis target device and are likely to cause a failure by performing failure diagnosis on each component member.
Automatically acquiring a first operation state signal indicating an operation state while the diagnosis target device is operating under the first operation condition using a sensor member;
Automatically acquiring a second operation state signal indicating an operation state when the diagnosis target device is operated under the second operation condition using a sensor member;
Based on the automatically acquired operation state signal indicating the first operation state and the operation state signal indicating the second operation state, the cause of the failure of the apparatus is modeled and analyzed, and the diagnosis is performed. The failure diagnosis method according to claim 1 , wherein failure candidates are narrowed down to one from a plurality of locations included in the diagnosis target device by performing failure diagnosis on each component member constituting the target device.
The failure diagnosis method according to claim 1, wherein the failure diagnosis is for diagnosing a cause of occurrence of a defect in an output image output from an image forming apparatus serving as the diagnosis target device.
A failure diagnosis device for diagnosing a failure that occurs in a diagnosis target device including a plurality of locations that may cause a failure,
Acquires operation state signal indicating the operating state during which the detection subject device is operating under a first operating condition,
When a failure occurs , based on the acquired operation state signal, by using the Bayesian network model, which is an analysis model that performs failure diagnosis based on the failure probability, modeling and analyzing the cause of the device failure, the individual components constituting the detection subject device performs a failure diagnosis, the detection subject device, wherein the first operating condition by operating under different second operating condition, said detection subject device An operation state signal indicating an operation state during operation under the second operation condition is acquired, and the acquired operation state signal is analyzed by modeling the cause of the failure of the apparatus, thereby performing the diagnosis by performing failure diagnosis for the individual components constituting the target device is included in the detection subject device, the failure candidates from a plurality of locations can cause failure 1 Fault diagnosis apparatus characterized by comprising a failure diagnosis section narrowed down to.
The failure diagnosis device according to claim 4 , wherein the failure diagnosis unit is provided in a device connected to the diagnosis target device via a communication line.
The failure diagnosis unit is an operation state signal that is automatically acquired using a predetermined sensor member in the diagnosis target device and that indicates an operation state while the diagnosis target device is operating under different operating conditions. The failure diagnosis apparatus according to claim 4 , further comprising an operation state signal receiving unit that receives the signal.
The failure diagnosis apparatus according to claim 4 , wherein the failure diagnosis diagnoses a cause of generating a defect in an output image output from an image forming apparatus as the diagnosis target apparatus.
An image inspection unit that analyzes the state of defects in the captured image by comparing the captured image obtained by optically reading the output image output from the image forming apparatus and a reference image for inspection;
The failure diagnosis apparatus according to claim 7 , wherein the failure diagnosis is performed based on a state of a defect analyzed by the image inspection unit.
The failure diagnosis unit performs the failure diagnosis by analyzing, based on the model, environmental information relating to the operation of the drive mechanism that is automatically acquired in the diagnosis target device. 4. The fault diagnosis apparatus according to 4 .
The failure diagnosis unit performs the failure diagnosis by analyzing, based on the model, history information of the operation state signal in the diagnosis target device, which is managed in the diagnosis target device. Item 5. The fault diagnosis apparatus according to Item 4 .
An image forming apparatus that includes a plurality of locations that can cause a failure and forms an image on a predetermined output medium,
A transport unit that transports the output medium using a predetermined drive mechanism;
An image forming unit that forms an image on the output medium transported to a predetermined position by the transport unit;
While the drive mechanism is operating under the first operating condition, the driving mechanism acquires an operating state signal indicating an operating state of each part in the image forming apparatus, and is different from the first operating condition. An operation state signal acquisition unit that acquires an operation state signal indicating an operation state of each unit in the image forming apparatus while operating under the operation condition of 2;
When a failure occurs, a failure diagnosis is performed based on a failure probability based on each of the operation state signals under the first operation condition and the second operation condition acquired by the operation state signal acquisition unit. Analyzing the cause of the failure of the apparatus by using a Bayesian network model, which is an analysis model to be performed, by performing a failure diagnosis on each constituent member constituting the image forming apparatus , the diagnosis target An image forming apparatus comprising: a failure diagnosis unit that narrows down failure candidates to one from a plurality of locations that are included in the device and may cause a failure .
An image inspection unit that analyzes a state of a defect in the captured image by comparing a captured image obtained by optically reading an output image formed by the image forming unit and a reference image for inspection;
The failure diagnosis apparatus according to claim 11 , wherein the failure diagnosis is performed based on a state of a defect analyzed by the image inspection unit.
An image reading unit that optically reads an output image formed by the image forming unit;
The image according to claim 12 , wherein the image inspection unit analyzes a state of a defect in the captured image by comparing the captured image read by the image reading unit with a reference image for inspection. Forming equipment.
An environmental information acquisition unit that acquires environmental information related to the operation of the drive mechanism using a sensor member;
The fault diagnosis unit, the environmental information acquired by using the sensor member in the environment information acquiring unit, by analyzing based on the model, according to claim 11, characterized in that performing the failure diagnosis Image forming apparatus.
The image forming apparatus according to claim 12 , wherein the environmental information acquisition unit acquires, as the environmental information, an ambient temperature and / or an ambient humidity of the constituent member of the drive mechanism.
A history storage unit that holds history information of the operation state signal in the diagnosis target device;
The failure diagnosis unit performs the failure diagnosis by analyzing history information of the operation state signal in the diagnosis target device, which is held in the history storage unit, based on the model. Item 12. The image forming apparatus according to Item 11 .
A program for diagnosing a failure that occurs in a diagnostic target device including a plurality of locations that may cause a failure using an electronic computer, the electronic computer,
A procedure of acquiring an operation state signal indicating an operation state while the diagnosis target device is operating under a first operation condition;
When a defect occurs , by analyzing the acquired operating state signal by modeling the cause of the failure of the device, a procedure for performing a failure diagnosis on each component member constituting the diagnosis target device,
A procedure for acquiring an operation state signal indicating an operation state while the diagnosis target device is operating under a second operation condition different from the first operation condition;
Based on the acquired operation state signal, the cause of the device failure is modeled and analyzed using a Bayesian network model, which is an analysis model for performing failure diagnosis based on the failure probability, thereby configuring the diagnosis target device A procedure for narrowing down failure candidates to one from a plurality of locations that are included in the diagnosis target device and may cause a failure by performing a failure diagnosis for each component member
A computer-readable storage medium storing a program for diagnosing a failure that occurs in a diagnosis target device including a plurality of locations that may cause a failure, using a computer,
An operation state signal indicating an operation state while the diagnosis target device is operating under the first operation condition is received from the diagnosis target device, and a second operation condition different from the first operation condition is received. An operation state signal receiving unit that receives an operation state signal representing an operation state of each unit in the image forming apparatus while operating below , from the diagnosis target device;
When a defect occurs , based on the respective operation state signals indicating the operation state while operating under the first operation condition and the second operation condition acquired by the operation state signal receiving unit, By using the Bayesian network model, which is an analysis model for performing failure diagnosis based on the failure probability, by modeling and analyzing the cause of the failure of the device, it is possible to perform failure diagnosis on the individual components constituting the diagnosis target device. A storage medium that stores a program that is included in the diagnosis target device to function as a failure diagnosis unit that narrows down failure candidates from a plurality of locations that may cause a failure .
JP2004125676A 2004-04-21 2004-04-21 Failure diagnosis method, failure diagnosis device, image forming device, program, and storage medium Expired - Fee Related JP4538845B2 (en)
JP2004125676A JP4538845B2 (en) 2004-04-21 2004-04-21 Failure diagnosis method, failure diagnosis device, image forming device, program, and storage medium
US11/064,028 US7243045B2 (en) 2004-04-21 2005-02-23 Failure diagnosis method, failure diagnosis apparatus, image forming apparatus, program, and storage medium
JP2005309078A JP2005309078A (en) 2005-11-04
JP4538845B2 true JP4538845B2 (en) 2010-09-08
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JP2004125676A Expired - Fee Related JP4538845B2 (en) 2004-04-21 2004-04-21 Failure diagnosis method, failure diagnosis device, image forming device, program, and storage medium
US (1) US7243045B2 (en)
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