Image forming apparatus that provides management apparatus with data that can be utilized for data analysis, control method for the image forming apparatus, storage medium, and management system

An image forming apparatus capable of providing a management apparatus with data that can be utilized for data analysis other than detection of signs of abnormality while keeping down costs required to build and maintain a communication environment. The image forming apparatus has a sensor. In the image forming apparatus, second data for use in detecting a sign of abnormality is generated based on first data comprised of measured values obtained by the sensor. The second data is transmitted directly or indirectly to the management apparatus that detects the sign of abnormality. Wherein, the second data is data that indicates characteristics of the image forming apparatus and has a smaller data amount that that of the first data.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to an image forming apparatus that provides a management apparatus with data that can be utilized for data analysis, a control method for the image forming apparatus, a storage medium, and a management system.

Description of the Related Art

A management system is known which monitors a status of an image forming apparatus and detects a sign of abnormality in the image forming apparatus based on information about the status of the image forming apparatus. In the management system, when a sign of abnormality in the image forming apparatus is detected, a maintenance person is requested to perform maintenance, and the maintenance person who has received the request performs maintenance of the image forming apparatus. By performing maintenance of the image forming apparatus when a sign of abnormality is detected, downtime caused by a failure of the image forming apparatus is avoided because appropriate actions can be taken before the image forming apparatus fails and becomes inoperative.

The management system is comprised of a management apparatus and a plurality of image forming apparatuses, and the management apparatus is connected to the plurality of image forming apparatuses via a network. For example, in the management system, the image forming apparatus transmits status information including a plurality of measured values obtained by various sensors provided in the image forming apparatuses to the management apparatus, which in turn accumulates the status information received from each of the image forming apparatuses (see, for example, Japanese Laid-Open Patent Publication (Kokai) No. 2011-166427). In this management system, the management apparatus calculates a feature value representing a status of one image forming apparatus based on status information received from the one image forming apparatus and detects a sign of abnormality in the one image forming apparatus based on a trend of the progression of the calculated feature value. In this management system, status information about the plurality of image forming apparatuses is collected in the management apparatus, and the status information includes a plurality of measured values obtained by the various sensors in the image forming apparatuses. For this reason, the status information can be utilized for data analysis other than prediction of a sign of abnormality. For example, the status information can be used to predict when maintenance of an image forming apparatus will be required (hereafter referred to merely as “the maintenance time”) before a sign of abnormality in the image forming apparatus is detected. On the other hand, since the status information includes a plurality of measured values as described above, data traffic increases when the image forming apparatus transmits the status information to the management apparatus, and significant costs are required to build and maintain a communication environment that implements such data communication. On the other hand, in another management system, the image forming apparatus calculates a feature value representing a status of the image forming apparatus based on status information and transmits information about a sign of abnormality detected based on a trend of the progression of the calculated feature value to the management apparatus (see, for example, Japanese Laid-Open Patent Publication (Kokai) No. 2020-3656). The information about the sign of abnormality does not include a plurality of measured values obtained by the various sensors described above but includes only limited information such as information that identifies a component whose sign of abnormality has been detected, and hence the information about the sign of abnormality has a smaller data amount than that of the status information. Therefore, the arrangement in which the image forming apparatus transmits information about a sign of abnormality to the management apparatus can reduce costs required to construct and maintain the communication environment as compared to the arrangement in which the status information is transmitted.

However, in the arrangement in which the image forming apparatus transmits information about a sign of abnormality to the management apparatus, information accumulated in the management apparatus is only limited information such as information that identifies a component whose sign of abnormality has been detected. For this reason, the information accumulated in the management apparatus cannot be utilized for data analysis other than detection of signs of abnormality. Namely, according to the prior art, it is impossible to provide the management apparatus with data that can be utilized for data analysis other tan detection of signs of abnormality while keeping down costs required to build and maintain the communication environment. It is also impossible to utilize the accumulated information in estimating the maintenance time for the image forming apparatus. Namely, according to the prior art, it is impossible to predict the maintenance time for the image forming apparatus while keeping down costs required to build and maintain the communication environment.

SUMMARY OF THE INVENTION

The present invention provides an image forming apparatus that is capable of providing a management apparatus with data that can be utilized for data analysis other than detection of signs of abnormality while keeping down costs required to build and maintain a communication environment, a control method for the image forming apparatus, a storage medium, and a management system.

Accordingly, the present invention provides an image forming apparatus with a sensor, comprising at least one memory that stores a set of instructions, and at least one processor that executes the instructions, the instructions, when being executed, causing the image forming apparatus to generate, based on first data comprising measured values obtained by the sensor, second data for use in detecting a sign of abnormality in the image forming apparatus, and transmit the second data directly or indirectly to a management apparatus that detects the sign of abnormality, wherein the second data is data that indicates characteristics of the image forming apparatus and has a smaller data amount than that of the first data.

According to the present invention, the management apparatus is provided with data that can be utilized for data analysis other than detection of signs of abnormality while keeping down costs required to build and maintain a communication environment. Moreover, according to the present invention, when maintenance of the image forming apparatus should be performed is predicted while costs required to build and maintain a communication environment are kept down.

DESCRIPTION OF THE EMBODIMENTS

The present invention will now be described in detail below with reference to the accompanying showing an embodiment thereof.

FIG. 1is a view schematically showing an arrangement of an abnormality prediction system100that is a management system according to the embodiment of the present invention. The abnormality prediction system100has one or more image forming apparatuses, a server103, and a management apparatus104. In the following description of the present embodiment, it is assumed that the abnormality prediction system100has, for example, two image forming apparatuses101and102. The image forming apparatuses101and102, the server103, and the management apparatus104are capable of communicating with one another via the Internet105. The abnormality prediction system100collects data from the image forming apparatuses101and102, and based on the collected data, detects signs of abnormality in the image forming apparatuses101and102.

The image forming apparatuses101and102, which are for example MFPs, have a plurality of functions such as a scanning function, a printing function, a copying function, and a fax communication function. In the present embodiment, the image forming apparatuses101and102have the same functions and arrangement, and hence the functions and arrangement of the image forming apparatus101will be described below as an example.

The image forming apparatus101receives a function selecting operation performed by a user and also executes a job submitted by the user. Examples of the job executed by the image forming apparatus101include a scan job, a print job, a copy job, and a fax transmission job. The image forming apparatus101transmits log data310and/or feature extraction data311inFIG. 3, which will be described later, required to detect a sign of abnormality in the image forming apparatus101, to the server103on a regular basis.

The server103stores (accumulates) the log data310and the feature extraction data311received from each of the image forming apparatuses101and102. The server103transmits the stored (accumulated) log data310and the stored (accumulated) feature extraction data311to the management apparatus104.

Upon receiving, for example, the log data310and the feature extraction data311of the image forming apparatus101from the server103, the management apparatus104analyzes the received feature extraction data311and detects a sign of abnormality in the image forming apparatus101. Specifically, the management apparatus104predicts failures, lifetimes, etc., of various components which the image forming apparatus101has. As a result of the prediction, when it is necessary to replace a component of the image forming apparatus101, the management apparatus104requests a maintenance inspector106to perform maintenance of the image forming apparatus101. Thus, in the present embodiment, regarding the image forming apparatus101to be managed by the abnormality prediction system100, maintenance such as replacement can be performed for the component approaching the end of its life before a component provided in the image forming apparatus101fails.

FIG. 2is a side view of the image forming apparatus101inFIG. 1. It should be noted that for ease of understanding, an internal arrangement of the image forming apparatus101is shown in perspective inFIG. 2. Referring toFIG. 2, the image forming apparatus101has a printer unit200and a reader unit240.

The reader unit240is a scanner that reads an image formed on an original245. The original245is placed on an original platen glass246such that its surface with an image formed thereon is in contact with the original platen glass246. The reader unit240transmits image data, which represents the read image, to the printer unit200. The reader unit240has a reading unit249and a reader image processing unit247.

The reading unit249is configured as one unit comprised of a light emitting unit242, an optical system243, and a light receiving unit244. The reading unit249, which is, for example, a line sensor extending toward the rear in the figure, reads an image on the original245while moving in a direction indicated by an arrow R248. The light emitting unit242illuminates the original245. The light receiving unit244receives light, which is reflected from the original245, via the optical system243. The light receiving result is transmitted to the reader image processing unit247. Based on the received light receiving result, the reader image processing unit247generates image data representing the image formed on the original245. The reader image processing unit247also functions as a sensor that measures an image density of the image formed on the original245based on the received light receiving result. The reader image processing unit247transmits the image data and the measured image density to the printer unit200.

The image forming apparatus101forms a color image through an electrophotographic method. The image forming apparatus101uses an intermediate transfer tandem method, and in the printer unit200, four image forming units Pa to Pd are disposed in tandem on an intermediate transfer belt206(transfer body). The image forming unit Pa forms a yellow toner image. The image forming unit Pb forms a magenta toner image. The image forming unit Pc forms a cyan toner image. The image forming unit Pd forms a black toner image. It should be noted that the number of colors formed is not limited to four.

Recording materials S such as sheets, each on which an image is formed, are stacked inside recording material cassettes230aand230bof the printer unit200. The recording material S is fed, when the image forming units Pa to Pd perform image forming, from the recording material cassette230a(or the recording material cassette230b) by sheet feeding rollers231a(or sheet feeding rollers231b) adopting the friction separating method. The sheet feeding rollers231aand231bconvey the recording materials S to registration rollers232via a conveying path. The registration rollers232correct for skewing of the recording materials S, adjust timing, and convey the recording materials S to a secondary transfer unit T2.

In the printer unit200, an image is formed by the image forming units Pa to Pd. In the present embodiment, the image forming units Pa to Pd have the same arrangement, and hence their arrangement will be described below using the image forming unit Pa as an example. The image forming unit Pa has a photosensitive body201a, a charging device202a, an exposure device203a, a developing device204a, a primary transfer unit T1a, and a photosensitive body cleaner205a. The charging device202auniformly charges a surface of the photosensitive body201awhich is rotationally driven. The exposure device203amodulates light based on image data received from the reader unit240and irradiates the photosensitive body201awith the modulated light. As a result, an electrostatic latent image corresponding to the image data is formed on the photosensitive body201a.

The developing device204adevelops the electrostatic latent image, which is formed on the photosensitive body201a, with a developer. In the present embodiment, toner is used as the developer. It should be noted that the developing device204aaccording to the present embodiment holds a two-component developer in which nonmagnetic toner and a magnetic carrier are mixed, but may hold a one-component developer comprised of magnetic toner or nonmagnetic toner. By toner being attached to the photosensitive body201aon which the electrostatic latent image is formed, a toner image is formed on the photosensitive body201a. When a predetermined amount of pressure and a predetermined amount of electrostatic load bias are applied to the primary transfer unit T1a, the primary transfer unit T1atransfers the toner image formed on the photosensitive body201ato the intermediate transfer belt206. Likewise, toner images formed on the photosensitive bodies201bto201dare transferred to the intermediate transfer belt206. Here, the toner images formed on the respective photosensitive bodies201ato201dare transferred to the intermediate transfer belt206such that they are superposed. Thus, the yellow, magenta, cyan, and black toner images are transferred to the intermediate transfer belt206such that they are superposed, forming a full-color toner image. Toner remaining on the photosensitive bodies201ato201dafter the transfer is collected by the photosensitive body cleaners205ato205d. In the printer unit200, when the amount of toner held in, for example, the developing device204ahas become equal to or smaller than a predetermined amount, the developing device204is replenished with toner from a toner bottle Ta which is a developer replenishment container.

The intermediate transfer belt206, which is provided on an intermediate transfer belt frame (not shown), is an endless belt stretched by a secondary transfer internal roller208, a tension roller212, and a secondary transfer upstream roller213. The intermediate transfer belt206is rotationally driven in a direction indicated by an arrow R207by the secondary transfer internal roller208, the tension roller212, and the secondary transfer upstream roller213. By rotating, the intermediate transfer belt206with the toner image in full color formed thereon conveys the toner image to the secondary transfer unit T2.

The recording material S and the toner image formed on the intermediate transfer belt206are conveyed with such timing that they join each other in the secondary transfer unit T2. The secondary transfer unit T2is a transfer nip unit formed by the secondary transfer internal roller208and a secondary transfer external roller209, which are disposed so as to face each other. By applying a predetermined amount of pressure and a predetermined amount of electrostatic load bias, the secondary transfer unit T2causes the toner image to be adsorbed onto the recording material S. The secondary transfer unit T2thus transfers the toner image on the intermediate transfer belt206onto the recording material S. Toner remaining on the intermediate transfer belt206after the transfer is collected by a transfer cleaner210.

The recording material S onto which the toner image has been transferred is conveyed from the secondary transfer unit T2to a fixing device211by the secondary transfer external roller209. The fixing device211applies a predetermined amount of pressure and predetermined-temperature heat to the recording material S within a fixing nip formed by rollers facing each other, and fuses and fixes the toner image on the recording material S. The fixing device211has a heater (not shown), which is a heat source, and is controlled to be maintained at an optimum temperature. The recording material S on which the toner image has been fixed is discharged onto a sheet discharge tray233. To form images on both sides of the recording material S, the recording material S is inverted by an inverting conveyance mechanism and conveyed to the registration rollers232, and another toner image is formed on a side of the recording material S on which the above toner image has not been fixed.

A density sensor220for detecting a toner density is provided in the vicinity of the intermediate transfer belt206. The density sensor220is disposed at a location where it is able to detect toner patterns of the respective colors formed on the intermediate transfer belt206, and more specifically, between the photosensitive body201dand the secondary transfer external roller209.

FIG. 3is a block diagram schematically showing a hardware arrangement of the image forming apparatus101inFIG. 1. Referring toFIG. 3, the image forming apparatus101has a control unit301, an operating panel304, a storage device307, and a network I/F312as well as the printer unit200and the reader unit240described above. The printer unit200, the reader unit240, the control unit301, the operating panel304, the storage device307, and the network I/F312are connected to one another via a data bus315.

The control unit301has a CPU302and a memory303. The control unit301integratedly controls operation of the image forming apparatus101. The CPU302is a hardware processor that executes various programs stored in the storage device307. For example, when power to the image forming apparatus101is turned on, the CPU302reads a program308stored in the storage device307and executes the read program308. As a result, the control unit301acts as a job control unit501and a data management unit503inFIG. 5, which will be described later. Also, a feature extraction data transmission control process inFIG. 9, which will be described later, is carried out by the CPU302executing the program308. The memory303is used as a work area for the CPU302and as a temporary storage area for each piece of data.

The operating panel304has a display unit305and an operating unit306. The display unit305is comprised of, for example, a color liquid crystal display, and displays various operating screens, which can be operated by the user and the maintenance inspector106, and information required for maintenance. The operating unit306is comprised of, for example, touch panel keys displayed on the display unit305and receives operations performed by the user and the maintenance inspector106.

The storage device307is a nonvolatile storage device and is, for example, a hard disk drive (HDD). The storage device307stores the program308, internal data309, log data310, and feature extraction data311. The internal data309is time-series data of sensor measured values obtained by various sensors which the reader unit240and the printer unit200have. The log data310is a data of, for example, job execution histories in the image forming apparatus101and includes detailed information about executed jobs, information about dates and times at which jobs were executed, and so forth. The feature extraction data311is generated based on the internal data309. The feature extraction data311is data indicating characteristics of the image forming apparatus101and has a smaller data amount than that of the internal data309. The network I/F312implements data communications via the Internet105. The image forming apparatus101carries out communications with the server103via the network I/F312.

The reader unit240has a sensor group313. The sensor group313includes a plurality of sensors which monitors operating states of movable components operating when the reader unit240reads an original. In accordance with requests received from the control unit301, the sensors included in the sensor group313output sensor measured values, which are obtained by measuring the operating states of the movable components, as one of pieces of the internal data309to the control unit301. The printer unit200has a sensor group314. The sensor group314includes a plurality of sensors, such as the density sensor220, which monitors operating states of movable components operating when the printer unit200forms an image. In accordance with requests received from the control unit301, the sensors included in the sensor group313output sensor characteristic values, which are obtained by measuring the operating states of the movable components, as one of pieces of the internal data309to the control unit301.

A description will now be given of a hardware arrangement of the server103and the management apparatus104. It should be noted that in the present embodiment, the server103and the management apparatus104have the same arrangement, and hence their arrangement will be described below by using the management apparatus104as an example.

FIG. 4is a block diagram schematically showing the hardware arrangement of the management apparatus104inFIG. 1. Referring toFIG. 4, the management apparatus104has a CPU401, a memory402, a storage device403, and a network I/F404. The CPU401, the memory402, the storage device403, and the network I/F404are connected to one another via a system bus405.

The CPU401is a central processing unit that controls the overall operation of the management apparatus104. The memory402stores an activation program for the CPU401and data required to execute the activation program. The storage device403has a larger capacity than that of the memory402and is, for example, an HDD. It should be noted that the storage device403is not limited to an HDD but may be another storage device having functions equivalent to those of the HDD, for example, a solid-state drive (SSD). The storage device403stores a control program which is executed by the CPU401.

To activate the management apparatus104, the CPU401executes an activation program stored in the memory402. This activation program is a program for expanding the control program stored in the storage device403into the memory402. Then, the CPU401executes the control program expanded into the memory402to perform various types of control. The CPU401uses the network I/F404to carry out data communications with other apparatuses such as the server103via the Internet105. For example, based on data received from the image forming apparatus101using the network I/F404, the management apparatus104is capable of sharing a screen displayed on the operating panel304of the image forming apparatus101and displaying this screen on a display unit of the management apparatus104.

FIG. 5is a block diagram showing a functional arrangement of the control unit301inFIG. 2. In the image forming apparatus101, the execution of the control program308by the CPU302causes the control unit301to function as the job control unit501and the data management unit503.

The job control unit501controls execution of a job in the image forming apparatus101. By controlling operation of the reader unit240and the printer unit200, the job control unit501controls execution of a job submitted by the user. The job control unit501includes a log recording unit502. When a job submitted by the user is executed, the log recording unit502records a job execution log as the log data310.

The data management unit503manages the internal data309and the feature extraction data311. The data management unit503includes a timing determination unit504, a data obtaining unit505, a feature extraction unit506, a data transmission deciding unit507, and a data transmission unit508.

The timing determination unit504determines whether or not it is time to transmit the feature extraction data311to the server103(hereafter referred to as “the data transmission time”). For example, when a predetermined time period set in advance has elapsed since the feature extraction data311was transmitted to the server103the last time (hereafter referred to as “the previous transmission of the feature extraction data311”), the timing determination unit504determines that it is the data transmission time.

The data obtaining unit505obtains, from the storage device307, the internal data309for use in generating the feature extraction data311which is to be transmitted to the server103. Specifically, the data acquisition unit505outputs data obtaining requests at predetermined times, which are defined for the respective sensors included in the sensor groups313and314described above, to the sensors and acquires sensor measured values from the respective sensors. It should be noted that the predetermined times may be every predetermined time, for example, interval of several milliseconds to several seconds or may be times before and after execution of a job submitted by the user. The data obtaining unit505obtains the log data310stored in the storage device307.

The feature extraction unit506carries out a feature extraction process for converting the internal data309obtained by the data acquisition unit505to generate the feature extraction data311. The data transmission deciding unit507carries out a data transmission deciding process inFIG. 11, which will be described later, to decide whether or not to transmit the generated feature extraction data311to the server103. When the data transmission deciding unit507has decided to transmit the feature extraction data311to the server103, the data transmission unit508transmits the feature extraction data311to the server103. Thus, in the present embodiment, the feature extraction data311is transmitted to the server103only when it is the data transmission time and the data transmission deciding unit507decides to transmit the feature extraction data311to the server103. As a result, when it is unnecessary to transmit the feature extraction data311to the server103, the image forming apparatus101can be prevented from transmitting unnecessary data to the server103, and hence communication load between the image forming apparatus101and the server103can be reduced.

FIGS. 6A and 6Bare views useful in explaining feature extraction data, which is generated by the abnormality prediction system100inFIG. 1, and a process relating to the feature extraction data.FIG. 6Ashows the relationship among the internal data309, feature extraction processes that are carried out by the image forming apparatus101, and abnormality prediction processes that are carried out by the management apparatus104.

Referring toFIG. 6A, data items601represent data items of the internal data309, and more specifically, names of items such as sensor measured values and count values obtained from the reader unit240and the printer unit200by the data obtaining unit505. In the present embodiment, IDs for identifying the respective data items are assigned to the respective data items of the internal data309.

Data sources602represent component elements in the image forming apparatus101which are sources of data in the data items601. Data types603represent attributes of the data in the data items601. Feature extraction processes604represent types of feature extraction processes in which the feature extraction data311is generated using the data in the data items601. InFIG. 6A, the feature extraction processes604for the data items601for which the feature extraction data311is not generated, like scan counter, print counter, and log data, are represented by “-” which means that no feature extraction process is carried out.

Determination processes605represent types of abnormality prediction processes which are carried out by the management apparatus104based on the feature extraction data311generated using the data in the data items601. In the abnormality prediction system100, the types of the abnormality prediction processes are managed in association with data items of data used to generate the feature extraction data311used to the abnormality prediction processes. Prediction request IDs606are unique numbers correspondingly assigned to the abnormality prediction processes which are the determination processes605. It should be noted that when the management apparatus104and the image forming apparatus101,102are configured to share the numbers of the prediction request IDs606, the numbers of the prediction request IDs606may be set for the respective abnormality prediction processes which are the determination processes605in advance, or the management apparatus104may regularly set the numbers of the prediction request IDs606for the respective abnormality prediction processes. Based on the numbers of the prediction request IDs606, the management apparatus104determines types of abnormality prediction processes to be carried out. For example, when the maintenance inspector106has instructed the management apparatus104to carry out an abnormality prediction process with a prediction request ID “3” so as to check a state of a transfer roller in the image forming apparatus101, the management apparatus104decides to carry out the abnormality prediction process corresponding to the prediction request ID “3”, which is for obtaining the dispersion ratio. The management apparatus104obtains the feature extraction data311corresponding to a running distance of the transfer roller, which is used to carry out the abnormality prediction process, from the server103, and carries out the abnormality prediction process for obtaining the dispersion ratio based on the obtained feature extraction data311.

FIG. 6Bis a view showing an example of transmission data607which the image forming apparatus101inFIG. 1transmits to the server103. The transmission data607is comprised of the multiple feature extraction data311. Referring toFIG. 6B, the transmission data607is comprised of data items608and specific values609. Feature extraction data are set as the data items608. Specific values for the feature extraction data in the data items608are set as the specific values609, for every generation time of the internal data309which is the basis of the feature extraction data. With this arrangement, it is possible to identify that the feature extraction data was generated based on which generation time of the internal data309. For example, a value “y” (80), which is a result obtained by carrying out a maximum value calculation process for calculating a maximum value of sensor measured values representing temperatures of the fixing device122measured from a measurement time “t” (01/01/2020/00:00:00) to a predetermined measurement time is set in the transmission data607inFIG. 6B. Also, as the result of carry out of a histogram creating process using a predetermined rule on a sensor measured value representing a running distance of the transfer roller until the measurement time “t” (01/01/2020/00:00:00), for example, that a classification group in a histogram creating process is to be (80) is set in the transmission data607inFIG. 6B. The image forming apparatus101converts the transmission data607to generate data in text format and also compresses the generated data in text format if necessary and transmits the compressed data to the server103.

FIG. 7is a sequence diagram useful in explaining the flow of a sequential process in which the feature extraction data311is generated, and notification of the need for maintenance is provided in the abnormality prediction system100inFIG. 1.

Referring toFIG. 7, the image forming apparatus101makes an internal data obtainment determination (step S701) to determine whether or not it is time to obtain sensor measured values and count values from the reader unit240and the printer unit200(hereafter referred to as “the internal data obtainment time”). Upon determining that it is the internal data obtainment time, the image forming apparatus101obtains data such as sensor measured values and count values from the reader unit240and the printer unit200(step S702) to generate the internal data309including the obtained data. In the internal data309, time-series data comprised of a plurality sensor measured values and count values are managed with respect to each item.FIG. 8Ashows rotational accelerations of a fixing belt motor at times T, which are examples of the time-series data comprised of the sensor measured values included in the internal data309, with a horizontal axis representing time (T) and a vertical axis representing rotational accelerations.

Next, the image forming apparatus101generates the feature extraction data311based on the internal data309comprised of the obtained sensor measured values and count values (step S703). For example, the image forming apparatus101generates the feature extraction data311inFIG. 8Bby carrying out a histogram creating process on the time-series data comprised of the rotational accelerations of the fixing belt motor inFIG. 8A, which are the sensor measured values included in the internal data309. Thus, by carrying out the histogram creating process on the rotational accelerations of the fixing belt motor, data indicating characteristics relating to appearance frequencies of the sensor measured values representing the rotational accelerations of the fixing belt motor and having a smaller data amount than that of the internal data309can be obtained.

Then, the image forming apparatus101carries out the data transmission deciding process inFIG. 11(step S704), which will be described later, to decide whether or not to allow transmission of the feature extraction data311. When deciding to allow transmission of the feature extraction data31, the image forming apparatus101transmits the feature extraction data311and the log data310to the server103(step S705). In the step S705, the image forming apparatus101may transmit the transmission data607comprised of the multiple feature extraction data311to the server103. Alternatively, the image forming apparatus101may transmit the feature extraction data311updated from previously transmitted data among the multiple feature extraction data311to the server103. After that, the image forming apparatus101carries out the process in the step S701. The image forming apparatus101thus repeatedly carries out the processes in the steps S701to S705.

Upon receiving the feature extraction data311and the log data310from the image forming apparatus101, the server103carries out a process in step S706. In the step S706, the server103updates the have-been-managed feature extraction data311and the log data310on the image forming apparatus101to the above-mentioned received feature extraction data311and log data310. Then, the server103stores the updated feature extraction data311and log data310(step S707). After that, the server103carries out the process in the step S706. The feature extraction data103thus repeatedly carries out the processes in the steps S706to S707.

The management apparatus104carries out a process in step S1201, which will be described later, to determine whether or not it is time to carry out an abnormality prediction process (step S708). When determining that it is time to carry out an abnormality prediction process, the management apparatus104obtains prediction data, which is required to carry out the abnormality prediction process, from the server103(step S709). The prediction data is the feature extraction data311and the log data310on the image forming apparatus101. Then, the management apparatus101carries out the abnormality prediction process associated with the obtained prediction data (step S710). For example, when obtaining, as the prediction data, the feature extraction data311inFIG. 8Bobtained by carrying out the histogram creating process on the rotational accelerations of the fixing belt motor, the management apparatus104carries out a determination process using the dispersion ratio in the histogram as the abnormality prediction process, based on the obtained feature extraction data311. For example, when the calculated dispersion ratio is equal to or greater than a predetermined dispersion ratio (e.g.,FIG. 8C), the management apparatus104determines that the fixing belt motor is normal. On the other hand, when the calculated dispersion ratio is smaller than the predetermined dispersion ratio (e.g.,FIG. 8D), the management apparatus104determines that there is a sign of abnormality in the fixing belt motor. It should be noted that although in the above description of the present embodiment, the method as an example was described, in which the management apparatus104performs the determination process on the feature extraction data311subjected to the histogram creating process, using the calculated dispersion ratio, the management apparatus104may carry out the determination process using another method using, for example, the average, the distortion ratio, and the kurtosis, not the dispersion ratio.

Referring again toFIG. 7, when determining that it is necessary to provide notification to the maintenance inspector106as a result of carrying out the abnormality prediction process, the management apparatus104provides notification to the maintenance inspector106(step S711). After that, the management apparatus104carries out the process in the step S708. The management apparatus104thus repeatedly carries out the processes in the step S708to S711.

FIG. 9is a flowchart showing the procedure of the feature extraction data transmission control process that is carried out by the image forming apparatus101inFIG. 1. The process inFIG. 9is implemented by the CPU302of the control unit301executing the program308. The process inFIG. 9is carried out at predetermined time intervals set in advance or on a regular basis at predetermined times set in advance. It should be noted that prior to the process inFIG. 9, the processes in the steps S701and S702described above have already been carried out, and the internal data309has already been generated.

Referring toFIG. 9, first, the control unit301carries out a feature extraction data generating process inFIG. 10(step S901), which will be described later, to generate the feature extraction data311(see the step S703). Next, the control unit301determines whether or not it is the data transmission time (step S902). In the step S902, for example, when a predetermined time period set in advance has elapsed since the previous transmission of the feature extraction data311, the control unit301determines that it is the data transmission time. On the other hand, when the predetermined time period has not elapsed, the control unit301determines that it is not the data transmission time.

As a result of the determination in the step S902, when it is not the data transmission time, the feature extraction data transmission control process proceeds to step S905. As a result of the determination in the step S902, when it is the data transmission time, the control unit301carries out process in step S903. In the step S903, the control unit301carries out the data transmission deciding process inFIG. 11, to be described later, to decide whether or not to allow transmission of the feature extraction data311to the server103(see the step S704).

When the transmission of the feature extraction data311to the server103is allowed in the step S903, the control unit301transmits the feature extraction data311generated in the step S901to the server103(step S904) (see the step S705). In the step S904, as described above, the control unit301may transmit the transmission data607comprised of the multiple feature extraction data311to the server103. Further, the control unit301may transmit the feature extraction data311updated since the previous transmission of the feature extraction data311among the multiple feature extraction data311to the server103. When the transmission of the feature extraction data311is completed, the feature extraction data transmission control process proceeds to the step S905. On the other hand, when transmission of the feature extraction data311to the server103is not allowed, the feature extraction data transmission control process proceeds to the step S905without the feature extraction data311being transmitted to the server103. In the step S905, the control unit301determines whether or not a job executing instruction given by the user has been received.

As a result of the determination in the step S905, when a job executing instruction given by the user has been received, the control unit301executes a job instructed to execute by the user (step S906). Upon completing the execution of the job, the control unit301updates the log data310(step S907). Specifically, the control unit301sets an execution record of the job in the log data310. Then, the control unit301transmits the updated log data to the server103(see the step S705). After that, the feature extraction data transmission control process is ended.

FIG. 10is a flowchart showing the procedure of the feature extraction data generating process in the step S901inFIG. 9.

Referring toFIG. 10, the control unit301reads the internal data309from the storage device307and determines whether or not the internal data309has been updated since the previous transmission of the feature extraction data311(step1001).

As a result of the determination in the step S1001, when the internal data309has been updated since the previous transmission of the feature extraction data311, the control unit301carries out a process in step S1002. In the step S1002, the control unit301identifies a data item that has been updated since the previous transmission of the feature extraction data311in the internal data309. Next, the control unit301determines a feature extraction process to be carried out (step S1003). For example, when the data item identified in the step S1002is “fixing unit temperature” inFIG. 6A, the control unit301determines that the feature extraction process to be carried out as a “maximum value calculation process” for generating feature extraction data of the identified item. In a case where a plurality of data items is identified in the step S1002, the control unit301determines feature extraction processes to be carried out for the respective ones of the identified data items.

Then, the control unit301determines whether or not data required to carry out the determined feature extraction process is included in the internal data309(step S1004). Here, for example, in the maximum value calculation process and a moving-average process, not only the latest data of the identified data item but also past data for a predetermined time period before that or a predetermined number of past data are required. Thus, in the present embodiment, since the number of data required to carry out varies with feature extraction processes, the number of data required to carry out each feature extraction process is managed in a management table (not shown). In the step S1004, it is determined whether or not the data required to carry out the determined feature extraction process including the past data is included in the internal data309.

As a result of the determination in the step S1004, when the data required to carry out the determined feature extraction process is included in the internal data309, the control unit301obtains data required to carry out the determined feature extraction process from the internal data309(step S1005). Then, the control unit301carries out the feature extraction process determined in the step S1003to generate the feature extraction data311(step S1006) and ends the feature extraction data generating process.

As a result of the determination in the step S1001, when the internal data309has not been updated since the previous transmission of the feature extraction data311, or as a result of the determination in the step S1004, when the data required to carry out the determined feature extraction process is not included in the internal data309, the feature extraction data generating process is ended without the feature extraction data311being generated.

FIG. 11is a flowchart showing the procedure of the data transmission deciding process in the step S903inFIG. 9.

Referring toFIG. 11, the control unit301reads the log data310from the storage device307(step S1101) and determines whether or not the log data310includes an execution record of jobs that have been executed since the previous transmission of the feature extraction data311(step S1102).

As a result of the determination in the step S1102, when the log data310includes an execution record of jobs that have been executed since the previous transmission of the feature extraction data311, the control unit301allows transmission of the feature extraction data311(step S1103) and ends the data transmission deciding process.

As a result of the determination in the step S1102, when the log data310does not include an execution record of jobs that have been executed since the previous transmission of the feature extraction data311, the control unit301carries out a process in step S1104. In the step S1104, the control unit301determines whether or not the feature extraction data311has been updated since the previous transmission, based on update date/time information included in the feature extraction data311.

As a result of the determination in the step S1104, when the feature extraction data311has been updated since the previous transmission, the data transmission deciding process proceeds to the step S1103. As a result of the determination in the step S1104, when the feature extraction data311has not been updated since the previous transmission, the control unit301prohibits transmission of the feature extraction data311(step S1105). Namely, in the present embodiment, when it is time to transmit the feature extraction data311and the feature extraction data311generated in the step S901is the same as feature extraction data transmitted the last time, the feature extraction data311generated in the step S901is not transmitted to the server103. After that, the data transmission deciding process is ended.

FIG. 12is a flowchart showing the procedure of an abnormality prediction control process that is carried out by the management apparatus104inFIG. 1. The process inFIG. 12is implemented by the CPU401of the management apparatus104executing a program stored in the memory402or the storage device403.

Referring toFIG. 12, the CPU401determines whether or not it is time to carry out an abnormality prediction process (step S1201). In the present embodiment, with respect to prediction request IDs of abnormality prediction processes that can be carried out by the management apparatus104, execution times such as predetermined time periods and predetermined times are set in advance. The management apparatus104can also receive a request to carry out an abnormality prediction process from the image forming apparatus101that is operated by the maintenance inspector106or the like. In the step S1201, when the time set in advance has come for an abnormality prediction process to be carried out, or when an execution request including a prediction request ID of an abnormality prediction process designated by the maintenance inspector106has been received from the image forming apparatus101or the like, the CPU401determines that it is time to carry out the abnormality prediction process. On the other hand, when the time set in advance has not come for an abnormality prediction process to be carried out and an execution request for an abnormality prediction process has not been received from the image forming apparatus101or the like, the CPU401determines that it is not time to carry out the abnormality prediction process.

As a result of the determination in the step S1201, when it is not time to carry out the abnormality prediction process, the abnormality prediction control process is ended. As a result of the determination in the step S1201, when it is time to carry out the abnormality prediction process, the CPU401obtains a prediction request ID for identifying the abnormality prediction process to be carried out. For example, when an execution request including a prediction request ID “2”, which has been transmitted from the image forming apparatus101so that the maintenance inspector106can grasp a state of the fixing belt was received, the CPU401obtains this prediction request ID “2”.

Next, the CPU401obtains the feature extraction data311and the log data310required to carry out an abnormality prediction process corresponding to the obtained prediction request ID (step S1202). Then, the CPU401carries out, based on the obtained feature extraction data311and log data310, the abnormality prediction process corresponding to the obtained prediction request ID (step S1203) (abnormality sign detection means).

For example, as the abnormality prediction process corresponding to the obtained prediction request ID “2”, the CPU401carries out a process in which it performs period analysis using the feature extraction data311that is generated by performing spectrum formation on time-series data on sensor measured values representing rotational accelerations of the fixing belt motor and determines whether or not an abnormality has occurred or there is a sign of abnormality. For example, when the period of a wave is equal to or smaller than a predetermined value as indicated by a dotted line1301inFIG. 13A, the CPU401determines that the fixing belt motor is normal. On the other hand, when the period of a wave is greater than the predetermined value as indicated by a solid line1302inFIG. 13A, the CPU401determines that there is a sign of abnormality in the fixing belt motor. Thus, in the present embodiment, whether or not there is a sign of abnormality in the image forming apparatuses101is determined based on the feature extraction data311, which indicates characteristics of frequency components in sensor measured values and has a smaller data amount than that of the internal data309.

Further, the CPU401determines whether or not there is a sign of abnormality by performing a inclination analyzing process using the feature extraction data311obtained by on time-series data of sensor measured values, which represents the speed of the intermediate transfer belt, having been subjected to the moving-average process. For example, referring toFIG. 13B, when the inclination of a waveform is equal to or smaller than a predetermined value, the CPU401determines that the intermediate transfer belt is normal. On the other hand, referring toFIG. 13C, when the inclination of a waveform is greater than the predetermined value, the CPU401determines that there is a sign of abnormality in the intermediate transfer belt. Thus, in the present embodiment, by using the feature extraction data311generated by the moving-average process being carried out on sensor measured values, the trend of the sensor measured values can be grasped with only a small amount of data, and also, measurement errors in the sensor measured values can be reduced.

Then, the CPU401determines, based on an execution result of the abnormality prediction process, whether or not to provide notification to the maintenance inspector106(step S1204). In the step S1204, for example, when occurrence of an abnormality or a sign of abnormality has been detected by the abnormality prediction process, the CPU401determines to provide notification to the maintenance inspector106. On the other hand, when occurrence of an abnormality or a sign of abnormality has not been detected by the abnormality prediction process, the CPU401determines not to provide notification to the maintenance inspector106.

In the step S1204, when the CPU401determines not to provide notification to the maintenance inspector106, the abnormality prediction control process is ended. In the step S1204, when the CPU401determines to provide notification to the maintenance inspector106, the CPU401generates an abnormal state notification including, for example, information about a component whose abnormality has been detected (step S1205). Then, the CPU401outputs the abnormal state notification for the maintenance inspector106(step S1206) and ends the abnormality prediction control process.

According to the embodiment described above, the image forming apparatus101(or the image forming apparatus102) transmits the feature extraction data311to the management apparatus104(indirectly) via the server103. The feature extraction data311has a smaller data amount than that of the internal data309. As a result, it is possible to keep down data traffic when the image forming apparatus101(or102) transmits data to the management apparatus104via the server103, and therefore, it is possible to keep down costs required to build and maintain a communication environment. The feature extraction data311is data indicating characteristics of the image forming apparatus101(or102). Therefore, for the image forming apparatus101(or102), it is possible to provide data that can be utilized for data analysis other than detection of a sign of abnormality. Namely, in the present embodiment, data that can be utilized for data analysis other than detection of a sign of abnormality can be provided to the management apparatus104while costs required to build and maintain a communication environment are kept down.

Moreover, in the embodiment described above, the abnormality prediction system100has the plurality of image forming apparatuses101and102. Thus, when the server103collects the feature extraction data311from each of a plurality of image forming apparatuses placed in many places, the processing load for transmitting the feature extraction data311can be reduced. As a result, in the abnormality prediction system100, processing can be efficiently performed when the server103collects the feature extraction data311as big data from many places around the world.

Furthermore, in the embodiment described above, the management apparatus104has the function of carrying out the abnormality prediction process. Here, in the abnormality prediction system100, when not the management apparatus104but the image forming apparatuses101and102are configured to have the function of carrying out the abnormality prediction process, a large-capacity storage device and a computation device, for implementing the function of carrying out the abnormality prediction process, need to be incorporated into each of the image forming apparatuses101and102. Therefore, regarding construct the abnormality prediction system100, it costs more in a case where the image forming apparatuses101and102have the function of carrying out the abnormality prediction process, than in the case where the management apparatus104has the function of carrying out the abnormality prediction process. In the present embodiment, the management apparatus104has the function of carrying out the abnormality prediction process. Thus, costs required to construct the abnormality prediction system100can be reduced as compared to the case where the image forming apparatuses101and102have the function of carrying out the abnormality prediction process.

In the embodiment described above, when it is time to transmit the feature extraction data311and the feature extraction data311generated in the step S901is the same data as feature extraction data transmitted the last time, the feature extraction data311generated in the step S901is not transmitted to the server103. Thus, in the abnormality prediction system100, transmission of unnecessary data such as transmission of data which the server103already holds from the image forming apparatus101(or102) to the server103can be prevented.

Moreover, in the embodiment described above, the feature extraction data311is data obtained by creating a histogram from the internal data309. Thus, data that has a smaller data amount than that of the internal data309and indicates characteristics relating to the appearance frequency of sensor measured values can be provided to the management apparatus104.

Furthermore, in the embodiment described above, the feature extraction data311is data obtained by performing spectrum formation on the internal data309. Thus, data that has a smaller data amount than that of the internal data309and represents characteristics relating to frequency components of sensor measured values can be provided to the management apparatus104.

Although the present invention has been described by way of the embodiment, the present invention should not be limited to the embodiment described above. For example, the abnormality prediction system100may have a structure in which the server103is not equipped and the image forming apparatus101,102is configured to transmit the feature extraction data311directly to the management apparatus104.

Moreover, although in the embodiment described above, the transmission data607obtained by aggregating the generated multiple feature extraction data311is transmitted to the server103, the present invention is not limited to this. For example, the generated multiple feature extraction data311may be individually transmitted to the server103.

Instead of the structure in the embodiment described above, the abnormality prediction system100may have a structure in which the management apparatus104obtains the latest feature extraction data311and at least one piece of the feature extraction data311generated prior to the generation of the latest feature extraction data311from the server103or the like and predicts a time when maintenance of the image forming apparatus101(or the image forming apparatus102) will be required (hereafter referred to as “the maintenance time”) based on the obtained multiple feature extraction data311. A description will now be given of an example in which a maintenance time for the image forming apparatus101is predicted based on the feature extraction data311(second data) on the density sensor220obtained from the server103.

FIG. 14is a block diagram schematically showing an arrangement of a printer control unit1400included in the printer unit200inFIG. 2. Referring toFIG. 14, the printer control unit1400has a CPU1401, a density sensor drive circuit1402, a shutter drive circuit1403, a density sensor detecting circuit1405, a ROM1407, and a RAM1408. The CPU1401is connected to the density sensor drive circuit1402, the shutter drive circuit1403, the density sensor detecting circuit1405, the ROM1407, and the RAM1408.

The CPU1401has a function of generating a command signal for performing density correction control using the density sensor220and a function of carrying out a computation process relating to the density correction control. The density sensor220, which is an optical sensor, detects densities of toner patterns formed on the intermediate transfer belt206. The density sensor drive circuit1402has a function of controlling turning on and off a light-emitting diode (hereafter referred to as the “LED”)1501and a photodiode (hereafter referred to as the “PD”)1502inFIGS. 15A and 15B, which the density sensor220has, and controlling driving current for the LED1501and the PD1502.

To perform the density correction control, the CPU1401controls the shutter drive circuit1403to transmit a drive signal to a shutter drive unit1401of the printer unit200. The shutter drive unit1401that has received this drive signal performs control to open a shutter1500inFIG. 15, to be described later, which keeps the density sensor220from becoming dirty. The CPU1401also controls the density sensor drive circuit1402to transmit a drive signal to the density sensor220. The density sensor220irradiates, based on the received drive signal, an object to be measured with light and detects reflected light from the object to be measured. The light detected by the density sensor220is subjected to I-V conversion. The density sensor circuit1405transmits signals indicating detection results received from the density sensor220to an A/D converter1406of the CPU1401. The A/D converter1406captures, in time series, the signals transmitted from the density sensor circuit1405, and subjects the captured signals to A/D conversion. The CPU1401performs computations for calculating density correction information by using a calculating formula stored in the ROM1407in advance and the signals subjected to the A/D conversion. The CUP1401, based on the calculated density correction information, determines setting values in a lookup table, and based on the determined setting values, updates values stored in the RAM1408in advance. To form an image, the CPU1401reads a setting value in the lookup table from the RAM1408and forms the image under a condition corresponding to the read setting value.

FIGS. 15A and 15Bare views useful in explaining how a toner pattern is detected by the density sensor101inFIG. 2. The density sensor220is disposed so as to face the intermediate transfer belt206as shown inFIG. 15Aand detects a toner pattern1504formed on the intermediate transfer belt206. The density sensor220is comprised of the LED1501that emits infrared radiation, the PD1502that receives infrared radiation, and an electric substrate (not shown) on which the LED1501and the PD1502are mounted. It should be noted that a light receiving unit of the density sensor220is not limited to the PD, but may be a photo transistor.

The LED1501is disposed so as to irradiate the intermediate transfer belt206with infrared radiation at an incidence angle of 20°. The PD1502is disposed so as to receive diffused reflected light1503of the light, which has been emitted to the intermediate transfer belt206and the toner pattern1504, at a reflection angle of −50°. These optical elements are mounted on the electric substrate (not shown) comprised of a drive circuit (not shown) that supplies electric current to the LED1501and a light receiving circuit (not shown) that has an conversion function of converting flowing current to voltage according to the amount of light received by the PD1502. It should be noted that in the present embodiment, the density sensor220is not limited to the above arrangement but has only to be an optical density sensor. For example, the density sensor220may, instead of being configured to detect the diffused reflected light1503from the toner pattern1504, be configured to detect light reflected from the intermediate transfer belt206and detect density using attenuation of light reflected from the intermediate transfer belt206according to the amount of toner attached to the intermediate transfer belt206.

There may be cases where paper dust derived from the conveyed recording material S and toner to be attached to the intermediate transfer belt206are scattered in the image forming apparatus101. If the scattered paper dust and toner become attached to the density sensor220, the amount of light emitted from and the amount of light received by the density sensor220will decrease, resulting in the accuracy of toner density detection by the density sensor220being decreased. To prevent the decrease in the accuracy of toner density detection by the density sensor220, the printer unit200has the shutter1500for keeping the density sensor220from becoming dirty. The shutter1500is disposed between the density sensor220and the intermediate transfer belt206. The shutter1500moves in a direction parallel to the density sensor220and the intermediate transfer belt206. The shutter1500is controlled to open and close by the shutter drive unit1404. For example, in a case where the density is to be detected, the shutter drive unit1404opens the shutter1500such that an opening of the shutter1500is formed at such a position as not to block light emitted from the density sensor220and reflected light to be received by the density sensor220(see, for example,FIG. 15A). On the other hand, in a case where the density is not to be detected, the shutter drive unit1404closes the shutter1500so as to block passage between an optical unit (the LED1501and the PD1502) of the density sensor220and the intermediate transfer belt206(see, for example,FIG. 15B).

As described above, in the present embodiment, the amount of dirt attached to the density sensor220can be considerably decreased by closing the shutter1500in the case where the density is not to be detected. However, in the case where the density is to be detected, the shutter1500is opened, and hence nothing blocks the passage between the optical unit of the density sensor220and the intermediate transfer belt206, resulting in paper dust and toner becoming attached to the density sensor220through the opening. As the amount of toner attached to the density sensor220increases, the amount of light emitted from and the amount of light received by the density sensor220gradually decreases. When the amount of light emitted from and the amount of light received by the density sensor220decreases, a detected value of toner density of the toner pattern1504becomes smaller than actual. That is, the accuracy of toner density detection by the density sensor220degrades.

To prevent such degradation in the accuracy of toner density detection by the density sensor220caused by attachment of paper dust and toner, in the printer unit200, light amount adjustment control is performed so as to increase the LED drive current and to keep the amount of light from the density sensor220constant. In the light amount adjustment control, the density sensor220irradiates a reference plate1505, which maintains its constant reflectivity, with light, and detects reflected light. The reference plate1505is mounted on a surface of the shutter1500which faces the density sensor220, as shown inFIG. 15B. The printer control unit1400controls the shutter drive unit1404to perform the light amount adjustment control while the reference plate1505being placed so as to face the optical unit of the density sensor220. It should be noted that in the present embodiment, the light amount adjustment control should not be limited the mentioned-above method using reflected light from the reference plate1505, and for example, may be performed using reflected light from the intermediate transfer belt206.

FIG. 16is a graph showing the relationship between LED driving current for the density sensor inFIG. 2and values detected by the density sensor220inFIG. 2. Referring toFIG. 16, the horizontal axis represents LED drive current values of the density sensor220, and the vertical axis represents values detected by the density sensor220. In the light amount adjustment control, the LED drive current values that are control values for controlling the amount of light from the LED1501are switched in five levels, and reflected light from the reference plate1505in each level of the LED drive current values is detected. InFIG. 16, I1to I5designate the LED drive current values in the five levels, and V1to V5designate values detected by the density sensor220when the LED drive currents I1to I5are supplied to the LED1501. Vt designates a value detected by the density sensor220and set as a target when the amount of light from the LED1501is adjusted. Namely, Vt is the value detected when the density sensor220has detected reflected light from the reference plate1505when an arbitrary LED drive current is supplied during initial installation of the image forming apparatus101. At the time of the initial installation, dirt derived from scattering of paper dust or toner is not attached to the density sensor220, and namely, Vt is the value detected when the amount of dirt is the least.

The printer control unit1400compares the measured V1to V5and Vt with each other and extracts two points sandwiching Vt, namely, the largest value among values smaller than Vt and the smallest value among values larger than Vt. Referring toFIGS. 16, V3and V4are extracted. The printer control unit1400linearly interpolates between the extracted V3and V4to calculate an LED drive current value It corresponding to Vt. The printer control unit1400sets the calculated LED drive current value It as an adjusted LED drive current value. Specifically, the printer control unit1400updates an LED drive current value for density correction stored in the RAM1408, to the calculated LED drive current value It. Thus, by setting the LED drive current value for density correction, to the calculated LED drive current value It, values detected by the density sensor220can be prevented from becoming smaller, and hence degradation in the accuracy of toner density detection by the density sensor220can be prevented. It should be noted that when the value of Vt is smaller than V1, or when the value of Vt is larger than V5, it is likely that the density sensor220could not normally detected density. For this reason, the LED drive current value for density correction, stored in the RAM1408, is not updated to the LED drive current value It calculated based on Vt.

FIG. 17is a flowchart showing the procedure of a light amount adjustment control process that is performed by the printer control unit1400inFIG. 14. The process inFIG. 17is implemented by the CPU1401of the printer control unit1400executing a program stored in the ROM1407or the like. The process inFIG. 17is carried out when a predetermined condition on which the characteristics of the density sensor220change is satisfied, for example, when execution of a job using the printer unit200is completed, when the image forming apparatus101is started, and when the image forming apparatus101returns from a power saving mode.

Referring toFIG. 17, first, the CPU1401moves the shutter1500to such that the reference plate1505faces the optical unit of the density sensor220(step S1701). Specifically, the CPU1401controls the shutter drive circuit1403to transmit a drive signal, which is an instruction to move the shutter1500, to the shutter drive unit1404. In accordance with the received drive signal, the shutter drive unit1404moves the shutter1500such that the reference plate1505faces the optical unit of the density sensor220. Next, the CPU1401controls the density sensor drive circuit1402to transmit a drive signal to the density sensor220and drive the density sensor220with the LED drive currents in the five levels (I1to I5) described above (step S1702). The CPU1401obtains the detected values V1to V5, which were obtained when the LED drive currents in the five levels (I1to I5) were supplied, from the density sensor220. Then, the CPU1401determines whether or not Vt lies within a range between V1and V5(step S1703).

As a result of the determination in the step S1703, when Vt lies within the range between V1and V5, that is, when V1is equal to or greater than V1and equal to or smaller than V5, the CPU1401extracts two detected values sandwiching Vt from V1to V5(step S1704). In the step S1704, the CPU1401extracts the largest detected value (for example, V3inFIG. 16) from detected values smaller than Vt among V1to V5and extracts the smallest detected value (for example, V4inFIG. 16) from detected values larger than Vt among V1to V5. Next, the CPU1401linearly interpolates between the extracted two detected values to calculate the LED drive current value It corresponding to Vt (step S1705). The calculated LED drive current value It is an LED drive current value for use in density adjustment from the next time. The LED drive current value for use in density adjustment will hereafter be referred to as “the light amount control value for density adjustment”. Then, the CPU1401sets the calculated LED drive current value It as the light amount control value for density adjustment from the next time (step S1706) and stores the set light amount control value in the RAM1408. The RAM1408stores a plurality of light amount control values which have been set in the past as well as the light amount control value set in the step S1706. After that, the CPU1401transmits the light amount control value set in the step S1706to the control unit301(step S1707) and ends the light amount adjustment control process.

As a result of the determination in the step S1703, when Vt does not lie within the range between V1and V5, that is, when V1is smaller than V1or larger than V5, the CPU1401determines that the density sensor220could not normally detect density. The CPU1401sets the light amount control value set in the previous light amount adjustment control process as the light amount control value for density adjustment from the next time (step S1708) and stores the set light amount control value in the RAM1408. After that, the CPU1401ends the light amount adjustment control process.

FIG. 18is a flowchart showing the procedure of a feature extraction data transmission process that is carried out by the image forming apparatus101inFIG. 1. The process inFIG. 18is implemented by the CPU302of the control unit301executing the program308. The process inFIG. 18is regularly carried out, for example, at predetermined time intervals set in advance or at predetermined times set in advance.

Referring toFIG. 18, the CPU302requests the printer control unit1400to transmit light amount data (step S1801). The light amount data includes a plurality of data such as a light amount control value set the last time and a light amount control values set prior to the last time. Next, the CPU302receives the light amount data from the printer control unit1400(step S1802) and stores the received light amount data in the memory303(step S1803). The memory303stores a plurality of light amount data received from the printer control unit1400in the past as well as the light amount data received in the step S1802. Then, the CPU302determines whether or not the number of data included in the light amount data received in the step S1802is a predetermined number set in advance (for example, 30) (step S1804).

As a result of the determination in the step S1804, when the number of data included in the received light amount data is the predetermined number (for example, 30), the CPU302carries out a data generating process inFIG. 19(step S1805), which will be described later, to generate the feature extraction data311. Then, the CPU302transmits the generated feature extraction data311to the server103(step S1806). It should be noted that in a case where the management apparatus104is configured to be capable of accumulating a plurality of feature extraction data311including past data, the CPU302may be configured to directly transmit the feature extraction data311to the management apparatus104as described above.

Then, the CPU302deletes the oldest light amount data among the plurality of light amount data stored in the memory303(step S1807). After that, the CPU302ends the feature extraction data transmission process.

As a result of the determination in the step S1804, when the number of data included in the received light amount data is not the predetermined number (for example, 30), the CPU302ends the feature extraction data transmission process without generating or transmitting the feature extraction data311.

It should be noted that in the above-described process inFIG. 18, the CPU302may determine, in the step S1804, whether or not the number of data included in the light amount data received in the step S1802is equal to or greater than a predetermined number set in advance (for example, 30). When the number of data included in the light amount data received in the step S1802is equal to or greater than the predetermined number (for example, 30), the feature extraction data transmission process proceeds to the step S1805. When the number of data included in the light amount data received in the step S1802is smaller than the predetermined number (for example, 30), the feature extraction data transmission process is ended.

FIG. 19is a flowchart showing the procedure of the data generating process in the step S1805inFIG. 18.

Referring toFIG. 19, the CPU302excludes a maximum value and a minimum value from data included in the light amount data received in the step S1802(step S1901). Next, the CPU302calculates an average value of data that was not excluded in the step S1901among the data included in the light amount data received in the step S1802(step S1902). By carrying out the processes in the steps S1901and S1902, variations in feature values of the feature extraction data311can be reduced. It can be considered that variations in the light amount control values are caused by, for example, changes in the amount of light emitted from the LED1501of the density sensor220arising from changes in the internal temperature of the image forming apparatus101.

Then, the CPU302normalizes the calculated average value (step S1903). Specifically, the CPU302divides the calculated average value by an upper limit value of a control range for the LED drive currents. The upper limit value of the control range is a value determined based on device characteristics of the LED1501. Here, a value obtained by the normalization in the step S1903is “1” when the average value calculated in the step S1902is equal to the upper limit value of the control range for the LED drive currents. Namely, when the value obtained by the normalization in the step S1903is “1”, the light amount adjustment control is not performed, and hence degradation in the accuracy of toner density detection by the density sensor220cannot be prevented. To prevent this situation, the abnormality prediction system100uses the value obtained by the normalization in the step S1903for calculating the maintenance time for the image forming apparatus101. It should be noted that a margin which the value obtained by the normalization in the step S1903has relative to “1” is a margin relative to the time when maintenance is required.

Then, the CPU302stores the value obtained by the normalization in the step S1903as the feature extraction data311in the memory303(step S1904). The feature extraction data311is data that indicates the feature of the light amount data on the density sensor220for calculating the maintenance time for the image forming apparatus101and is also data with a smaller data amount than that of the light amount data including a plurality of data. After that, the data generating process is ended.

FIG. 20is a flowchart showing the procedure of a maintenance time notification process that is carried out by the management apparatus104inFIG. 1. The maintenance time notification process inFIG. 20is implemented by the CPU401of the management apparatus104executing a program stored in the memory402or the storage device403. It should be noted that in the present embodiment, the management apparatus104carries out the maintenance time notification process at a timing set in advance, for example, at predetermined time intervals, predetermined times, and so forth. Further, the management apparatus104carries out the maintenance time notification process when receiving a request to carry out the maintenance time notification process from the image forming apparatus101or the like which is operated by the maintenance inspector106or the like.

Referring toFIG. 20, the CPU401receives the feature extraction data311on the density sensor220from the server103(or directly from the image forming apparatus101or the like) and stores the received feature extraction data311on the density sensor220in the storage device403(step S2001). Next, the CPU401determines whether or not the number of feature extraction data311on the density sensor220stored in the storage device403is two or more (step S2002). In the step S2002, for example, when the feature extraction data311on the density sensor220stored in the step S2001and at least one piece of feature extraction data311on the density sensor220which was received prior to the feature extraction data311on the density sensor220stored at the step S2001are stored in the storage device403, the CPU401determines that the number of feature extraction data311on the density sensor220stored in the storage device403is two or more. On the other hand, when no feature extraction data311on the density sensor220other than the feature extraction data311on the density sensor220stored in the step S2001is stored in the storage device403, the CPU401determines that the number of feature extraction data311on the density sensor220stored in the storage device403is not two or more.

As a result of the determination in the step S2002, when the number of feature extraction data311on the density sensor220stored in the storage device403is two or more, the CPU401calculates the maintenance time for the image forming apparatus101based on the latest feature extraction data311on the density sensor220stored in the storage device403and at least one piece of feature extraction data311on the density sensor220received prior to the latest feature extraction data311(step S2003). For example, the CPU401calculates, in a way of extrapolation, a date and time at which the feature value becomes equal to “1” using the latest feature extraction data311on the density sensor220stored in the storage device403(for example, a feature value Cn inFIG. 21), the second latest feature extraction data311on the density sensor220stored in the storage device403(for example, a feature value Cn-1inFIG. 21), and dates and times at which the respective pieces of feature extraction data311are generated (for example, Tn, Tn-1inFIG. 21). The date and time at which the feature value becomes equal to “1” means a date and time at which it becomes impossible to perform the light amount adjustment control and to prevent degradation in the accuracy of toner density detection by the density sensor220. It is necessary to perform maintenance of the density sensor220by this date and time. The CPU401sets the calculated date and time as a time limit for maintenance to be performed and calculates a time period between the time limit for maintenance to be performed and one month before that as the maintenance time for the image forming apparatus101with consideration that the maintenance inspector106make a maintenance plan.

Then, the CPU401notifies the maintenance inspector106of the calculated maintenance time (step S2004) and ends the maintenance time notification process.

As a result of the determination in the step S2002, when the number of feature extraction data311the density sensor220stored in the storage device403is not two or more, the CPU401ends the maintenance time notification process without providing notification of the maintenance time.

According to the embodiment described above, the management apparatus104obtains the latest feature extraction data311on the density sensor220generated by the image forming apparatus101and at least one piece of feature extraction data311on the density sensor220generated prior to the latest feature extraction data311, and based on the obtained multiple feature extraction data311on the density sensor220, predicts the maintenance time for the image forming apparatus101. The feature extraction data311on the density sensor220is data that indicates features of the light amount data on the density sensor220. Thus, the maintenance time for the image forming apparatus101can be predicted based on variations in the light amount data on the density sensor220. Further, the feature extraction data311on the density sensor220has a smaller data amount than that of the light amount data including a plurality of data. For this reason, it is possible to keep down data traffic when the management apparatus104receives data from the server103or the like, and therefore, it is possible to keep down costs required to build and maintain the communication environment. Namely, in the present embodiment, it is possible to predict the maintenance time for the image forming apparatus101while keeping down costs required to build and maintain the communication environment.

In the embodiment described above, the feature extraction data311on the density sensor220is data obtained by dividing the average value, which is obtained by averaging at least a part of the plurality of light amount setting values included in the light amount data, by the upper limit value of the control range for the LED drive currents. The time when the density sensor220is needed to be performed maintenance on can be calculated using such data indicating characteristics of the density sensor220based on which it is possible to determine whether or not to perform the light amount adjustment control in the image forming apparatus101. As a result, the maintenance time for the image forming apparatus101equipped with the density sensor220can be predicted.

Moreover, in the embodiment described above, extrapolation is used to predict the maintenance time for the density sensor220based on a plurality of accumulated feature extraction data311on the density sensor220. As a result, the maintenance time for the image forming apparatus101can be predicted easily using the plurality of feature extraction data311on the density sensor220accumulated in the server103or the like.

Furthermore, in the embodiment described above, the density sensor220is a sensor that detects the densities of toner patterns formed on the intermediate transfer belt206. Therefore, maintenance of the image forming apparatus101can be performed before occurrence of a failure such as the density sensor220becomes unable to detect the densities of toner patterns formed on the intermediate transfer belt206.

In the embodiment described above, since the feature extraction data311is accumulated in the server103, the accumulated feature extraction data311(hereafter referred to as “the accumulated data”) can be utilized to develop new technology. A description will now be given of an example in which the accumulated data is utilized to add a function of identifying failed parts.

In a case where a factor that causes a change in the characteristics of the density sensor220is dirt attached to the density sensor220as described above, the characteristics of the density sensor220have a tendency of slowly changing over a certain period of time. On the other hand, in a case where a factor that causes a change in the characteristics of the density sensor220is a failure of the density sensor220, the characteristics of the density sensor220have a tendency of sharply changing at the timing when the density sensor220has failed. For example, when the shutter drive unit1404of the density sensor220has failed, it becomes impossible to move the shutter1500to an appropriate position where the reference plate1505faces the optical unit of the density sensor220at the timing when the shutter drive unit1404is failed. Namely, in the light amount adjustment control, the density sensor220becomes unable to detect reflected light from the reference plate1505as intended. As a result, a light amount control value that greatly differs from a light amount control value set the last time is set in the light amount adjustment control. Namely, the characteristics of the density sensor220sharply change. In the present embodiment, details of required maintenance are predicted based on such tendencies of changes in the characteristics of the density sensor220. For example, when the characteristics of the density sensor220change relatively slowly, it is predicted that maintenance involving removal of dirt from the density sensor220will be required. On the other hand, when the characteristics of the density sensor220sharply change, it is predicted that maintenance involving repair of the shutter drive unit1404will be required.

Moreover, by comparing the accumulated data in the server103and the failure logs for the image forming apparatus101with each other, it is possible to find out tendency of change in data when a failure occurs. The function of identifying failed parts based on the accumulated data can be developed and implemented, by finding out the tendency of data at the timing when a failure occurs. With this function, the maintenance inspector106can be notified of a failed part in advance and head for a maintenance with preparing a replacement part, to smoothly perform maintenance.

Although in the embodiment described above, the maintenance time for the image forming apparatus101is predicted based on the plurality of feature extraction data311on the density sensor220generated at different times, the type of the feature extraction data311is not limited to the feature extraction data311on the density sensor220. The feature extraction data311may be any type as long as changes in the characteristics can be determined by comparing a plurality of feature extraction data311of the same type generated at different times.

Other Embodiments

This application claims the benefit of Japanese Patent Application No. 2020-130873, filed on Jul. 31, 2020 and Japanese Patent Application No. 2020-132484, filed on Aug. 4, 2020, which are hereby incorporated by reference herein in their entirety.