Maintenance system, maintenance server, and maintenance method using a prediction model

A maintenance system includes a plurality of apparatuses and a maintenance server. Each apparatus: transmits log data indicating a state of the apparatus to the maintenance server; receives a prediction model from the maintenance server, the prediction model predicting the occurrence of an abnormal state of the plurality of apparatuses; determines whether the abnormal state of the apparatus occurs based on the prediction model to generate a determination result; and transmits the determination result indicating the occurrence of the abnormal state of the apparatus to the maintenance server. The maintenance server: generates the prediction model based on the log data received from each of the plurality of apparatuses; and issues an instruction of a maintenance work for one or more of the plurality of apparatuses that transmit the determination result.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is based on and claims priority pursuant to 35 U.S.C. § 119(a) to Japanese Patent Application No. 2018-044806, filed on Mar. 12, 2018, in the Japan Patent Office, the entire disclosure of which is hereby incorporated by reference herein.

BACKGROUND

Technical Field

The present invention relates to a maintenance system, a maintenance server, and a maintenance method.

Description of the Related Art

An apparatus such as a multifunction peripheral is increasingly sophisticated and complicated. Thus, it is sometimes difficult for a user to handle abnormality or failure. In such a case, a customer engineer or the like visits the customer to handle the abnormality or the failure. However, urgent maintenance work increases cost borne by an apparatus manufacturer and generates downtime that prevents the user from using the apparatus.

To handle such a problem, a technique for facilitating appropriate handling of the abnormality or the failure has been devised. For example, the apparatus accesses a maintenance server when detecting a maintenance event, acquires maintenance information, which introduces the maintenance work corresponding to the detected maintenance event, from the maintenance server, and displays maintenance information on a display.

SUMMARY

Example embodiments of the present invention include a maintenance system, including a plurality of apparatuses, each of the plurality of apparatuses including first circuitry, and a maintenance server to manage maintenance work of the plurality of apparatuses, the maintenance server including second circuitry. The first circuitry: transmits log data indicating a state of the apparatus to the maintenance server; receives a prediction model from the maintenance server, the prediction model predicting the occurrence of an abnormal state of the plurality of apparatuses; determines whether the abnormal state of the apparatus occurs based on the prediction model to generate a determination result; and transmits the determination result indicating the occurrence of the abnormal state of the apparatus to the maintenance server. The second circuitry: generates the prediction model based on the log data received from each of the plurality of apparatuses; and issues an instruction of a maintenance work for one or more of the plurality of apparatuses that transmit the determination result.

DETAILED DESCRIPTION

There has been a demand for a system capable of predicting the abnormality or the failure of the apparatus before the abnormality or the failure of the apparatus actually occurs. If a maintenance server or the like predicts occurrence of the failure before the apparatus actually fails, a customer engineer or the like repairs the apparatus in periodic inspection work before the occurrence of the failure. In such a case, downtime of a multifunction peripheral can be reduced, and a frequency of urgent maintenance work can be reduced. Thus, service cost can be reduced. In the case where occurrence of abnormality can be predicted in addition to the occurrence of the failure, the same effect can be expected.

For example, similar abnormality or failure is likely to occur to the same model. Thus, a need for prediction of the abnormality or the failure of the model of apparatuses tends to be high for the model of apparatuses that are highly available in a market. For example, a server or the like can predict the abnormality or the failure based on apparatus information from the apparatus before the abnormality or the failure of the apparatus occurs.

The customer engineer or the like of an apparatus manufacturer can set in advance a state of the apparatus where the abnormality or the failure is likely to occur to the apparatus in the future. However, a large volume of data is required to identify correlation between a sign of the abnormality or the failure of the apparatus and the actual abnormality or failure. Thus, it is difficult for the apparatus manufacturer to sufficiently reflect such correlation to the state of the apparatus. It is also difficult to set the state of the apparatus corresponding to the unknown abnormality or the unknown failure. Thus, the predictable abnormality or the predictable failure is also limited.

In view of the above, according to one or more embodiments, which will be described below, a maintenance system capable of predicting that the apparatus is no longer in a normal state can be provided.

A description will hereinafter be made on a maintenance system, a maintenance server, and a maintenance method performed by the maintenance system as an example of a mode for carrying out the present invention with reference to the drawings.

<Schematic Operation of Maintenance System>

FIG. 1is an example schematic explanatory view of operation of a maintenance system100according to the present embodiment. (1) An apparatus10collects log data indicating a state of the apparatus10, and transmits the collected log data to a maintenance server30periodically or upon operation or the like as a trigger. (2) The maintenance server30applies a machine learning algorithm to the log data and creates a learning model (a prediction model) predicting occurrence of abnormality or failure of the apparatus10. (3) The maintenance server30distributes the learning model to each of the apparatuses10. (4) The apparatus10inputs the log data (more specifically, apparatus monitoring data) to the learning model distributed from the maintenance server30. Then, the apparatus10determines whether the apparatus10is likely to fail or whether the abnormality is likely to be detected (hereinafter simply referred to as whether the abnormality or the failure is predicted). (5) In the case where the abnormality or the failure is predicted, the apparatus10notifies the maintenance server30of predicted one of the abnormality and the failure. The maintenance server30informs a person in charge of customer service to make him/her perform maintenance work of a portion, the abnormality or the failure of which is predicted.

According to such a configuration, the learning model, which is set in the apparatus10, can predict the abnormality or the failure from the log data before the occurrence of the abnormality or the failure. Thus, the customer engineer or the like can handle predicted one of the abnormality and the failure in periodic inspection. As a result, the frequency of the urgent maintenance work can be reduced. In addition, a component or the like can be replaced before the abnormality or the failure occurs. Thus, the downtime that prevents a customer from using the apparatus10is less likely to occur.

Furthermore, the maintenance server30continues learning by using the log data. Thus, the learning model for predicting the abnormality or the failure can be developed without being limited to a preset detection condition of the abnormality or the failure.

In the case where the maintenance server30uses the learning model to predict the abnormality or the failure, the maintenance server30has to collect the log data, which is used to predict the abnormality or the failure, from each of the apparatuses10, and has to predict the abnormality or the failure for each of the apparatuses10. As a result, a load on the maintenance server30is increased. In the present embodiment, each of the apparatuses10predicts the abnormality and the failure. Thus, concentration of the load on the maintenance server10can be reduced.

In this disclosure, the prediction model is a function of predicting in advance that the apparatus is brought into an abnormal state. For example, the prediction model is a program outputting a magnitude of a possibility that the apparatus is brought into the abnormal state with respect to data input representing the state of the apparatus. In the present embodiment, the prediction model will be described by using terms such as a detector, the learning model, and learning model data.

The abnormal state is any state that differs from a normal state. Examples of the abnormal state include the abnormality, the failure, trouble, and a defect. The abnormal state may be referred to as a not-normal state. In the present embodiment, the abnormal state will be described by using terms such as the abnormality and the failure.

In the present embodiment, a sign, prediction, and estimation of the abnormal state may have the same meaning unless otherwise specified.

Issuance of a maintenance work instruction is arrangement of a service to improve or repair the abnormality or the failure of the apparatus10whose abnormality or failure is predicted.

System Configuration Example

FIG. 2is an example of a schematic configuration diagram of the maintenance system100according to the present embodiment. The maintenance system100includes one or more apparatuses10and the maintenance server30communicable with each other via a network N. The network N is constructed of some or all of a local area network (LAN) established in a facility where the apparatus10exists, wide area Ethernet (Registered Trademark), a wide area network (WAN) in which the LANs are connected to each other, a virtual private network (VPN), a telephone network of a telecommunications carrier, the Internet, and the like.

Each of an apparatus10-1to an apparatus10-n(collectively referred to as the apparatus10) is an apparatus operated by a user and is also an apparatus, the abnormality or the failure of which can occur. One or more of the apparatuses10are provided for the single customer. The customer is, for example, organization, institution, organization, company, company, organization, cooperative, federation, coalition etc. Examples of the customer are an organization, an institution, a council, a company, a corporation, a group, a cooperative, a federation, and an association.

The apparatus10assists the user with efficient business operation when the user inputs/outputs electronic information used for business in/from the apparatus10. In the apparatus10, application software or browser software can be operated. The apparatus10has a communicating function via the network N. The apparatus10is the multifunction peripheral, for example. The multifunction peripheral is an apparatus having a plurality of functions such as a scanner function, a printer function, a copier function, and a facsimile (FAX) transmitting/receiving function. The multifunction peripheral may also be referred to as a multi-function printer/product/peripheral (MFP), a can print copy (SPC), and an all-in-one (AIO). In the present embodiment, the apparatus10may not have the plurality of functions. The apparatus10may be a scanner, a printer (a printing apparatus), a copier, a FAX machine, or the like.

The apparatus10is not limited to the multifunction peripheral and may be an apparatus capable of transmitting the log data to the maintenance server30. For example, the apparatus10may be a teleconference terminal, an electronic blackboard, a projector, or the like. In addition to the above, the apparatus10may be an information processing apparatus such as a personal computer (PC), a tablet computer, a smartphone, a personal digital assistant (PDA), a gaming machine, a navigation terminal, or a wearable PC.

The maintenance server30is the information processing apparatus (a server) that provides information and a function to the apparatus10through the network N. The maintenance server30learns the log data acquired from the apparatus10, and develops the learning model for predicting the occurrence of the abnormality or the failure of the apparatus10. The learning model is distributed to each of the apparatuses10. When the apparatus10uses this learning model to predict the occurrence of the abnormality or the failure, the apparatus10notifies the server30of the predicted abnormality or the predicted failure. Then, the maintenance server30executes processing to instruct the customer engineer, who performs the periodic inspection of the apparatus10, to handle the predicted abnormality or the predicted failure such as replacement of the component as a cause of the abnormality or the failure, the occurrence of which is predicted. As an example of the processing, the maintenance server30notifies a terminal possessed by the customer engineer of a number of the apparatus10that the abnormality is predicted to occur, and urges the customer engineer to handle the predicted abnormality.

The maintenance server30may be compatible with cloud computing. The cloud computing is a usage mode in which a resource on the network is used without being conscious of a specific hardware resource. In the case where the maintenance server30is compatible with the cloud computing, a physical configuration of the maintenance server30in the present embodiment is not restricted. The maintenance server30may be configured that the hardware resource is dynamically connected/disconnected in accordance with the load or the like.

FIG. 3is an example of a schematic hardware configuration diagram of the maintenance server30. The maintenance server30includes a CPU201and memory202that allows high-speed access to data used by the CPU201. The CPU201and the memory202are connected to another device or driver of the maintenance server30, such as a graphics driver204and a network driver (NIC)205, via a system bus203.

The graphics driver204is connected to a liquid-crystal display (LCD)206via a bus, and monitors a processing result of the CPU201. The network driver205connects the maintenance server30to the network N at a transport layer level and a physical layer level, so as to establish a session with the apparatus10.

An I/O bus bridge207is connected to the system bus203. A storage device such as a hard disk drive (HDD)209is connected to a downstream side of the I/O bus bridge207via an I/O bus208such as Peripheral Component Interconnect (PCI) by Integrated Drive Electronics (IDE), Advanced Technology Attachment (ATA), ATA Packet Interface (ATAPI), serial ATA, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like. The HDD209stores a program209pfor controlling the entire maintenance server30. The HDD209may be a solid-state drive (SSD).

An input device210such as a keyboard or a mouse (referred to as a pointing device) is connected to the I/O bus208via a bus such as the USB, and receives input and a command by an operator such as a system administrator.

The illustrated hardware configuration of the maintenance server30may not be accommodated in a single casing or provided as a unitary apparatus, but rather includes hardware elements preferably provided in the maintenance server30. In addition, since the maintenance server30is compatible with the cloud computing, the physical configuration of the maintenance server30in the present embodiment may not be fixed. The maintenance server30may be configured that the hardware resource is dynamically connected/disconnected in accordance with the load.

<<Hardware Configuration of Apparatus>>

FIG. 4is an example block diagram illustrating a schematic hardware configuration of the apparatus10. In the apparatus10, a controller410is connected to each of a facsimile control unit (FCU)412, a USB413, the Institute of Electrical and Electronics Engineers (IEEE) 1394 interface414, an engine unit (Engine)415, and sensors417via a PCI bus416.

The controller410controls the entire apparatus10to perform rendering, communication, or control input from an operation panel411. The engine unit415is a printer engine or the like that can be connected to the PCI bus416, and is a monochrome plotter, a one-drum-color plotter, a four-drum-color plotter, a scanner, or a facsimile unit, for example.

In addition to a so-called engine such as the plotter, the engine unit415includes an image processing unit for performing error diffusion, gamma conversion, and the like.

The sensors417are various sensors, each of which detects the apparatus monitoring data. For example, the sensors417include a paper detection sensor, a current sensor, a temperature sensor of a fixing unit, and a clock.

The controller410includes a CPU401, a north bridge (NB)403, a system memory (MEM-P)402, a south bridge (SB)404, local memory (MEM-C)407, an application specific integrated circuit (ASIC)406, and a HDD408. The NB403and the ASIC406are connected by an accelerated graphics port (AGP) bus405.

The MEM-P402includes a read only memory (ROM)402aand a random access memory (RAM)402b.

The CPU401executes overall control of the apparatus10, has a chip set including the NB403, the MEM-P402, and the SB404, and is connected to the other apparatus10via the chip set.

The NB403is a bridge that connects the CPU401to the MEM-P402, the SB404, and the AGP bus405. The NB403has: a memory controller that controls reading from and writing to the MEM-P402; a PCI master; and an AGP target.

The MEM-P402is a system memory used as a memory for storing a program and data, a memory for loading the program and data, a drawing memory for the printer, or the like. The MEM-P402includes the ROM402aand the RAM402b.

The ROM402ais a read only memory used as a memory for storing the program and the data. The RAM402bis a writable and readable memory used as a memory for loading the program and the data, a drawing memory for the printer, or the like.

The SB404is a bridge used to connect the NB403to a PCI device and a peripheral device. The SB404is connected to the NB403via a PCI bus, and a network I/F409and the like are also connected to this PCI bus. The ASIC406is an integrated circuit (IC) that is used for image processing and has a hardware element for the image processing. The ASIC406plays a role as a bridge that connects the AGP bus405, the PCI bus416, the HDD408, and the MEM-C407.

The ASIC406includes: a PCI target and an AGP master; an arbiter (ARB) as a core of the ASIC406; a memory controller controlling the MEM-C407; a plurality of direct memory access controllers (DMACs) used to apply rotation on image data and the like by a hardware logic and the like; and a PCI unit transferring data between the ASIC406and the engine unit415via the PCI bus416.

The network I/F409is a communication device for communicating with the maintenance server30and the like via the network N, and is a network interface card (NIC), for example.

The FCU412, the USB413, and the IEEE 1394 interface414are connected to the ASIC406via the PCI bus416.

The operation panel411is directly connected to the ASIC406. The MEM-C407is the local memory used as a copy image buffer and a code buffer. The HDD408is a storage that accumulates image data, a program, font data, and a form.

The HDD408also stores a program408pexecuted by the apparatus10. The AGP bus405is a bus interface for a graphics accelerator card and is proposed to speed up graphic processing. The AGP bus405directly accesses the MEM-P402with high throughput to accelerate the graphics accelerator card.

The illustrated hardware configuration of the apparatus10is merely one example. For example, the apparatus10in which the operation panel is implemented by an information processing apparatus such as a tablet terminal, or the like may be adopted. In addition, the hardware configuration may differ by the apparatus10. In addition to the illustrated configuration, the apparatus10may include a microphone, a temperature sensor, a humidity sensor, an acceleration sensor, and the like. The data detected by these components can also be the log data.

FIG. 4is the hardware configuration diagram of the case where the multifunction peripheral is implemented as the apparatus10. However, the hardware configuration may vary depending on a type of the apparatus10.

FIG. 5is an example functional block diagram illustrating functions of the apparatus10and the maintenance server30provided in the maintenance system100.

The apparatus10includes an abnormality/failure detecting unit11, a basic functioning unit12, a display control unit14, a log data transmitting unit15, a state monitoring unit16, a learning model receiving unit17, a sign notifying unit18, a sign detection result recording unit19. The apparatus10also includes a detector13for controlling distribution of the learning model data. Each of these functions of the apparatus10is implemented by the program408p, which is loaded onto the MEM-P402from the HDD408for execution by the CPU401illustrated inFIG. 4. The program408pmay be distributed from a program distribution server or may be distributed in a state of being stored in a portable storage medium such as a USB memory stick or an optical storage medium.

The basic functioning unit12provides basic functions of the apparatus10in accordance with the user operation or the like. For example, the basic functioning unit12provides the printer function, the scanner function, the copier function, and the FAX transmitting/receiving function. The basic functioning unit12also controls actuators (a motor, a clutch, and the like) providing the functions, turning ON/OFF of a switch, a temperature of the fixing unit, the sensors, and the like.

The state monitoring unit16monitors the basic functioning unit12and acquires the apparatus monitoring data representing a state of the apparatus10. The apparatus monitoring data preferably includes the state of the apparatus10estimated to be highly relevant to the abnormality or the failure. For example, the apparatus monitoring data includes paper passing time, a current value of the motor, the temperature of the fixing unit, a value of a register of the ASIC406, and the like. The state monitoring unit16stores a service call, which will be described next, and the apparatus monitoring data as the log data in a log data storage unit21.

The abnormality/failure detecting unit11monitors the abnormality or the failure of the basic functioning unit12. In the present embodiment, the abnormality or the failure is notified to the maintenance server30by predetermined information referred to as the service call. Each of the abnormality and the failure is a mode of the state of the apparatus10. While the state (the apparatus monitoring data) simply means the state of the apparatus10, the service call means occurrence of some kind of problem. Each of many service calls indicates occurrence of the problem that is difficult to be solved by the user alone. In addition, the service call serves as teacher data when the learning model is developed. In the present embodiment, at least one of paper jam and document jam (hereinafter referred to as paper jam) included in the apparatus monitoring data is not a target of the service call. However, the paper jam serves as the teacher data. In the present embodiment, the abnormality or the failure can be predicted by the learning model before occurrence of such abnormality or failure that the service call or the paper jam is detected.

The log data transmitting unit15transmits the log data stored in the log data storage unit21to the maintenance server30at predetermined timing. Examples of the predetermined timing are timing immediately after the service call or the paper jam is detected, periodical timing (hourly, half daily, daily, weekly, or the like), timing at which a certain volume of the log data is accumulated, and timing at which a specific operation is detected.

The learning model receiving unit17receives the learning model data generated by the maintenance server30and sets the learning model data in the apparatus10. As will be described later, the learning model data includes a filter, a maximum value extraction parameter, and weight between nodes. A function or means for predicting the abnormality or the failure by using the learning model data will be referred to as the detector13. The number of the detectors13is preferably the same as the number of types of the service calls and the number of types of the paper jams. The detector13predicts the abnormality or the failure from the apparatus monitoring data. The detector13can predict the abnormality or the failure before the abnormality/failure detecting unit11detects the actual abnormality or the actual failure. In the case where the detector13predicts the abnormality or the failure, the display control unit14displays a sign detection screen, which will be described later, on the operation panel411.

The sign notifying unit18transmits a sign detection result of the abnormality or the failure detected by the detector13to the maintenance server30. The sign detection result is information notifying that the abnormality or the failure which has not occurred is likely to occur in the future. The sign detection result includes a service call number, a detection date and time, an apparatus number, and the like. The apparatus number includes model information of the apparatus10, and is identification information that uniquely specifies or identifies the apparatus10. In addition, the sign notifying unit18monitors sign record data, which will be described later. In the case where the sign detection result in which a maintenance completion date is recorded is present, the sign notifying unit18transmits maintenance performance notification to the maintenance server30.

The sign detection result recording unit19records prediction of the abnormality or the failure detected by the detector13as the sign detection result in sign detection record data22. The sign detection record data22is stored in the storage unit20, which will be described next.

The apparatus10includes the storage unit20implemented by the HDD408or the MEM-P402illustrated inFIG. 4, or the like. The log data storage unit21is constructed in the storage unit20, and the storage unit20stores the sign detection record data22. The log data storage unit21stores the log data (the apparatus monitoring data and the service call) collected by the state monitoring unit16. The log data in the log data storage unit21is deleted after being transmitted to the maintenance server30. The log data will be described with reference toFIG. 6.

Table 1 illustrates an example of the sign detection record data22. The sign detection record data22is a record of when and what sign is detected in the apparatus10and when the maintenance is performed. The sign detection record data22has items of a sign detection date and time, the service call number, and the maintenance completion date. The sign detection date and time is a date and time when the detector13predicts the abnormality or the failure. The service call number is identification information that identifies or specifies the type of the service call. When the paper jam is adopted as the teacher data, the type of the paper jam is set in the item of the service call. However, in the case of the paper jam, the maintenance by the customer engineer or the like is unnecessary. Thus, the maintenance completion date can be blank. The maintenance completion date is a date and time when the maintenance is completed for the service call in which the abnormality or the failure is predicted.

The maintenance server30includes a log data receiving unit31, a learning model transmitting unit32, a sign receiving unit33, a maintenance content recording unit34, a learning unit35, a service arranging unit36, and a report generating unit37. Each of these functions of the maintenance server30is a function or means implemented by the program209p, loaded onto the memory202from the HDD209for execution by the CPU201illustrated inFIG. 3. This program209pmay be distributed from the program distribution server or may be distributed in the state of being stored in the portable storage medium such as the USB memory stick or the optical storage medium.

The maintenance server30includes a storage unit40implemented by the HDD209or the memory202illustrated inFIG. 3, or the like. A log data accumulating unit41and a data selection table storage unit42are constructed in the storage unit40, and the storage unit40stores maintenance content data43. The log data accumulated by the log data accumulating unit41is the same as the log data transmitted by the apparatus10. Meanwhile, the log data from each of the apparatuses10is accumulated in the maintenance server30.

Table 2 illustrates an example of the maintenance content data43. The maintenance content data43is a record of when and what abnormality or failure is detected in each of the apparatuses10and when the maintenance is performed. Thus, the maintenance content data43has the same contents as the sign detection record data22except for the apparatus number.

Table 3 illustrates an example of a data selection table stored in the data selection table storage unit42. The data selection table is a table used to determine the log data of the model to be used in development of the learning model. The data selection table has items of the service call number and one or more apparatus groups. The apparatus group includes one or more of the models that can be regarded as the same apparatuses10in regard to the service call or the paper jam. The service call or the paper jam is the identification information that identifies the specific abnormality or the specific failure. However, it is considered that likelihood of the occurrence of the abnormality or the failure varies by the model even with the same service call or the paper jam. Meanwhile, the different models, for which the same components or the same mechanism is adopted, may be regarded as the same model. That is, the data selection table is a table in which the group of the models that can be regarded to be the same in regard to the service call or the paper jam is associated. In this way, the learning model can be developed for each of the components or each of the mechanisms mounted on the apparatus. Thus, prediction accuracy of the abnormality or the failure can be improved.

Referring back toFIG. 5, the description continues. The log data receiving unit31receives the log data from the apparatus10and accumulates the log data in the log data accumulating unit41constructed in the storage unit40. The learning unit35uses the log data accumulated in the log data accumulating unit41to generate the learning model that detects the sign of the abnormality or the failure.

The learning model transmitting unit32transmits the learning model data to the apparatus10. The sign receiving unit33receives the sign detection result (a determination result) of the abnormality or the failure, which is detected by the apparatus10using the detector13, from the apparatus10. The service arranging unit36arranges the service for the apparatus10that the abnormality or the failure is predicted. Such arrangement of the service will be referred to as issuance of the maintenance work instruction. For example, the service arranging unit36designates the service call number, and arranges replacement of the component related to the service call number, or the like by the customer engineer for the customer who uses the apparatus10. In this way, the abnormality or the failure can be prevented within a range of the periodic inspection before the abnormality or the failure of the apparatus10actually occurs.

The sign receiving unit33further receives the maintenance performance notification from the apparatus10. The sign detection result and the maintenance performance notification are transmitted to the maintenance content recording unit34, and the maintenance content recording unit34records the sign detection result and the maintenance performance notification in the maintenance content data43.

The report generating unit37reads the maintenance content data43and prepares a report on the maintenance. The report generating unit37aggregates the service call numbers notified in a certain period (a week, a month, a quarter, or the like) to prepare the report, and transmits the report to each maintenance work site. As a result, for example, the service call number, the number of which is large, can be found out. Thus, the component or the like for this service call can be prepared in advance at a service center or the like.

FIG. 6is a schematic view of an example of the log data.FIG. 6illustrates time-series log data in an acquisition order of the log data. The apparatus10constantly collects the data (the apparatus monitoring data and the service call) indicating the state of the local apparatus10. Various causes of the abnormality or the failure of the apparatus10are present. In order to detect the abnormality or the failure, an occurring condition of which is unclear, the various states of the apparatus10that are high relevant to the abnormality or the failure are preferably monitored in advance and recorded as the log data. The log data illustrated inFIG. 6includes following contents. An occurrence date and time are recorded in all the log data.

An example of the service call (SC) that indicates the occurrence of the abnormality or the failure and is output in a form of “SC+number”. From this number, the content (the type) of the service call is obtained.

The log data that records duration from initiation of paper conveyance to time at which the paper passes each paper passing sensor. That is, the duration for the paper to pass SENSOR_A is obtained from an elapsed period from time at which SENSOR_A is turned ON to time at which SENSOR_A is turned OFF. In the case where duration of the paper conveyance is longer than standard duration, the cause of the abnormality or the failure may be hidden.

The log data including a name and the current value of the motor. MOTOR_A is a paper conveyance motor name, and 0.155 mA is the current value. The large current value indicates that the paper is not smoothly conveyed. Thus, in the case where the current value of the paper conveyance motor is large, the cause of the abnormality or the failure may be hidden.

FIXING 30 Degree

The log data in which a temperature (30 degrees) of the fixing unit (FIXING) is recorded. In the case where the temperature of the fixing unit is excessively high or is not increased, the cause of the abnormality or the failure may be hidden.

The log data in which a value (50) set in a register (REGISTER_A) of the ASIC for image processing by the CPU and set time are recorded. The value set in the register (REGISTER_A) of the ASIC is a number of the filter used for the image processing, a number of a gamma conversion table, or the like. The correct value has to be set in the register before the paper passes a determined position. The paper passing timing is recorded in the log data such as of SENSOR_A ON, SENSOR_A OFF, and the like. Thus, a timing deviation (delay) can be detected from this timing, the value of the register, and the set time. In the case where the timing delays, the cause of the abnormality or the failure may be hidden.

The log data illustrated inFIG. 6is merely one example, and the information that can be acquired from the apparatus10can be set as the log data.

FIG. 7is an example sequence diagram illustrating a procedure of transmitting the log data to the maintenance server30by the apparatus10.

S11: The log data transmitting unit15of the apparatus10uses a predetermined communication protocol to transmit a log data transmission request of the apparatus10to the maintenance server30. The predetermined protocol may be any protocol such as Hypertext Transfer Protocol Secure (HTTPS), HTTP 2.0, HTTP, File Transfer Protocol (FTP), or Web Distributed Authoring and Versioning (WebDAV).

S12: If the maintenance server30is ready to receive the log data, the maintenance server30transmits an approval to the apparatus10. For example, in the case where a load on the maintenance server30side is increased by another task or the like, the maintenance server30rejects the transmission of the log data. If the data transmission is rejected, the apparatus10makes the transmission request again after a lapse of a certain period.

S13: The log data transmitting unit15of the apparatus10transmits data attributes such as size, a data compression scheme, and an encryption scheme of the log data to be transmitted to the maintenance server30.

S14: The log data receiving unit31of the maintenance server30secures memory to receive the log data, and notifies the log data transmitting unit15of the apparatus10of preparation completion of the data reception. If sufficient memory size to allow the reception of the log data is not secured, the log data receiving unit31of the maintenance server30notifies the log data transmitting unit15of the apparatus10to divide and transmit the data.

S15: The log data transmitting unit15of the apparatus10starts transmitting the log data.

S16: The log data receiving unit31of the maintenance server30determines whether the complete log data is received on the basis of the size. If the complete log data is received, the log data receiving unit31of the maintenance server30transmits completion of the data reception to the apparatus10.

<Development of Learning Model>

FIG. 8is an example of an explanatory view of training data and the teacher data used to develop the learning model. The learning unit35receives the apparatus monitoring data and the service call (also receives the paper jam, which is not illustrated inFIG. 8) included in the log data. The apparatus monitoring data is the training data, and the service call is teacher data. The learning model can be expressed as an approximation function representing a relationship between the apparatus monitoring data and the service call. When the learning model is generated, the service call can be output with respect to the received apparatus monitoring data.

The maintenance server30collects and holds the apparatus monitoring data of all of the apparatuses10connected to the network. The learning unit35of the maintenance server30uses the log data collected from the apparatuses10to generate the learning model, which detects the sign of the occurrence of the specific abnormality and the specific failure, by machine learning. Various methods are available for the machine learning algorithm. In recent years, in the machine learning by deep learning that shows a favorable result of image recognition or the like, a person does not have to find a characteristic amount. A learner can create the learning model when being provided with the training data.

When creating the learning model, the maintenance server30according to the present embodiment uses some of the log data of all the apparatuses10connected to the network as the training data, and similarly uses the service call, which is included in the log data and indicates the occurrence of the abnormality such as the paper jam or the failure preventing the use of the apparatus10, as the teacher data. The service call indicates the occurrence of the abnormality or the failure that is not handled by the apparatus10itself, and the number (the SC number) is assigned for each type of the service call. The service call may be recorded in the log data and may also be displayed on an operating unit such as the operation panel411.

The conventional service call usually notifies the user of dispatch of the customer engineer or the like, who performs the maintenance work, by a supplier providing the maintenance work, so as to handle the occurrence of the failure or the abnormality that is not repaired by the control of the apparatus10itself.

The log data itself sequentially records the apparatus monitoring data and the service calls in a chronological order. Meanwhile, it may take long time to use all of the acquired log data for learning, and the correct learning model may not be created. To handle such a problem, the apparatus monitoring data, which is generated in a certain period prior to the occurrence of the specific service call or a specific event (such as the paper jam) to be learned, is used as the training data. As a result, the apparatus monitoring data that is highly relevant to the predicted abnormality or the predicted failure can be extracted. This extracted apparatus monitoring data is used as the training data, and a probability vector representing the occurrence of the service call (or the paper jam) is used as the teacher data. The probability vector is a vector in which detected one of the types of the service call and the type of the paper jam is set to 1 and the other is set to 0.

The certain period in which the apparatus monitoring data is extracted may be adjusted and determined appropriately. In the case where the certain period occurs too early (too old) from the generation timing of the service call, correlativity between the apparatus monitoring data and the service call (or the paper jam) is low. Thus, it is difficult for the learning unit35to learn correspondence between the apparatus monitoring data and the service call (or the paper jam). Meanwhile, in the case where the apparatus monitoring data that is generated close to the generation timing of the service call is used, a hidden cause of the service call that occurs prior to the generation timing of such apparatus monitoring data is not used for learning. Thus, the favorable certain period is preferably and appropriately determined from an experiment. This certain period may vary by the type of the service call or the type of the paper jam.

For the learning, in addition to the training data of the specific apparatus10that transmitted the service call, the service call issued by the other apparatus10as the model set in the data selection table is also used.

In addition, after a certain volume of the training data is used for the learning, the log data (here, verification data) at the time when the new service call is generated is used to confirm detection accuracy of the learning model to predict the occurrence of the service call.

In the case where the detection accuracy of the prediction (a correct answer rate for the verification data) reaches predetermined accuracy or higher, the learning unit35concludes that the learning model for predicting the occurrence of the service call is created, and terminates the learning. Then, the learning unit35extracts the learning model data from the learning model, and the detector13of the apparatus10uses the learning model data to predict the abnormality or the failure. In the case where the detection accuracy of the prediction does not reach the predetermined accuracy or higher, the learning unit35continues learning until the detection accuracy of the prediction exceeds reference accuracy. The learning unit35creates and holds the learning model for each of the numbers of service calls and each of the types of the paper jam. The learning model is distributed to the apparatus10.

In the case where the detection accuracy is not improved even with the certain volume of the training data, the learning unit35stops learning and notifies a manager of the maintenance server30. In this case, the learning model is not distributed. In addition to the case where the sign of the abnormality or the failure such as the service call is detected, the learning model can also be used for the case where a sign of an increase in an occurrence frequency of the event such as the paper jam that hinders the normal operation is detected.

FIG. 9is an explanatory view of a method for creating the learning model. The various algorithms are available for the machine learning. In the present embodiment, a description will be made on the deep learning (a neural network), in which the person does not have to find the characteristic amount of the training data suited for the prediction of the abnormality or the failure, as an example.FIG. 9illustrates a general configuration example of the deep learning.

The deep learning has three layers of an input layer901, a hidden layer902, and an output layer903. The hidden layer902has one or more layers. The neural network with the large number of the hidden layers will be referred to as the deep learning. A “circle” in each of the layers indicates a node of the neural network. From the input layer901toward the output layer903, all the nodes of the immediately preceding layer are connected to the single node of the next layer. The connection between the nodes will be referred to as an edge.

The hidden layer902has convolutional layers902a,902c, pooling layers902b,902d, and fully bonding layers902e,902f. A configuration of each of the layers differs by the adopted algorithm. For example, the number of the convolutional layers902aand902c, the number of the pooling layers902band902d, and the number of the fully bonding layers902eand902fare not limited to the illustrated numbers.

In the present embodiment, each of the nodes of the input layer901corresponds to each piece of the apparatus monitoring data, and each of the nodes of the output layer903corresponds to the event such as the service call or the paper jam. The maximum number of the nodes of the output layer903is a sum of the types of the service calls and the types of the paper jam. When the apparatus monitoring data is entered in the input layer901, each of the nodes of the output layer903outputs a value. Here, a sum of the values of the nodes of the output layer903is “1”. Thus, the value of each of the nodes of the output layer903is the probability vector (0.01, 0.02, . . . , 0.90). The probability vector is a vector, a sum of which becomes 1. The occurrence of the service call or the paper jam assigned to the node having the largest value has the highest probability. A learning method of the neural network will be described later.

In the present embodiment, the neural network is used as the example. However, the neural network may not be used, and the algorithm of the mechanical learning such as linear regression, logistic regression, a support vector machine, a decision tree, random forest, Ada Boost, a naive base, or a k neighborhood method may be used for the learning.

FIG. 10is an explanatory view of a method for creating the learning model of the neural network. The occurrence of the paper jam is used as an example of teacher data904for convenience of the description. The cause of the occurrence of the paper jam differs by a location of the occurrence. Thus, the paper jam is distinguished as paper jam A, paper jam B . . . in accordance with the location of the occurrence (each of the paper jam A and the paper jam B is an example of paper jam location information). There are the various causes of the occurrence of the paper jam. Examples of the causes are: the case where the paper slips during the paper conveyance due to wear of a paper feeding roller and results in a delay of paper feeing timing; and a reduction in torque due to deterioration of a paper feeding motor. In any of such cases, paper passing timing gradually deviates, which facilitates the occurrence of the paper jam. In the case where the paper jam is likely to occur due to deterioration of the component of the apparatus10, the increase in the occurrence frequency of the paper jam can be detected from the apparatus monitoring data such as the paper passing timing.

In view of the above, here, data on initiation timing of the paper conveyance and data on the current value of the motor, which are considered to be highly relevant to the event to occur, are extracted herein from the log data immediately before the occurrence of the paper jam and used as the training data. As described with reference toFIG. 6, in addition to the apparatus monitoring data of the same apparatus10, the apparatus monitoring data of the other apparatus10, to which the same service call or the same paper jam occurs, and which belongs to the same group in the data selection table, is also used as the training data.

One piece of the apparatus monitoring data is input to each of the nodes of the input layer901. Correspondence between the node and the apparatus monitoring data is fixed, and the determined apparatus monitoring data is input to the certain node.

The teacher data904at the time of the occurrence of the paper jam A is expressed by the above-described probability vector (1, 0, 0, 0). That is, “1” is input to the node corresponding to the paper jam A due to the occurrence of the paper jam A while “0” is input to each of the nodes corresponding to the paper jam B to D due to absence of the paper jam B to D. A difference between each of these values and the output value of the corresponding node of the output layer (for example (0.7, 0.02, 0.15, 0.13) is calculated. Then, weight between the nodes of the neural network is adjusted so as to reduce the difference. The network is optimized by one of a backpropagation method, a gradient method, and the like.

FIG. 11Ais an explanatory view of a configuration of the deep learning.FIG. 11Ais an example of an explanatory view of convolution and pooling. The apparatus monitoring data is convoluted as an M×N matrix. M represents the number of the apparatus monitoring data, and N represents the maximum character number of the apparatus monitoring data. When the apparatus monitoring data is treated as the matrix just as described, text data can be handled in a same manner to the image data. A plurality of the smaller matrices than the text data is created by the convolution (filter calculation with duplicated elements), and each of the matrices is pooled (a maximum value is extracted from a certain range of the matrix). In this way, each of the matrices gradually becomes small. InFIG. 11A, the matrix becomes 2×2 in the end. It is considered that the characteristics are summarized in the 2×2 matrix. Then, the 2×2 matrix is made to be one-dimensioned for input to the fully bonding layer. Processing in the fully bonding layer may be the same as processing of the normal neural network.

For a purpose of simplification,FIG. 11Billustrates the apparatus monitoring data in which M is 7 and N is 5. (1) In the convolution layer, the apparatus monitoring data is convoluted by using two each of 4×5, 3×5, and 2×5 filters. The size and the number of each of the filters are merely examples. As a result, two each of 4×1, 5×1, and 6×1 matrices are acquired. (2) In the pooling layer, the maximum value of each of the two 4×1 matrices is taken out, the maximum value of each of the two 5×1 matrices is taken out, and the maximum value of each of the two 6×1 matrices is extracted. (3) In the one-dimensional layer, three 2×1 matrices are combined into one vector. (4) It is considered that the characteristics of the apparatus monitoring data is summarized in such a vector, and the vector is classified by the fully bonding layer (the neural network). That is, the probability of each of the service calls is calculated. InFIG. 11B, the probabilities of the two service calls are calculated. The weight between the nodes of the fully bonding layer may be learned by the backpropagation method.

Thus, the learning model data includes the filter of the convolution layer, the maximum value extraction parameter of the pooling layer, and the weight between the nodes of the fully bonding layer.

The certain volume of the training data is used to create the learning model. Thereafter, several pieces of the unused log data are prepared to calculate a correct answer rate of an output result at the time when the apparatus monitoring data in the log data is input to the learning model. In the case where a learning termination criterion is set to be equal to or higher than 70% of the detection accuracy, ten pieces of the log data, which are not used for the learning, are provided. Then, when the seven or more correct answers are acquired, the learning is terminated.

<Distribution Procedure of Learning Model>

FIG. 12is an example sequence diagram illustrating a procedure of distributing the learning model to the apparatus10by the maintenance server30.

S21: The learning model transmitting unit32of the maintenance server30uses the communication protocol of the network to transmit a learning model transmission request to the apparatus10.

S22: If the learning model receiving unit17of the apparatus10is ready to receive the data, the learning model receiving unit17sends an approval. For example, in the case where the load of the apparatus10is increased due to the operation of the apparatus10by the user, or the like, the learning model receiving unit17rejects the transmission of the learning model. In the case where the transmission of the learning model is rejected, the learning model transmitting unit32of the maintenance server30makes the transmission request again after a lapse of a certain period.

S23: The learning model transmitting unit32of the maintenance server30notifies the apparatus10of the data attributes such as the service call number, the data size, the data compression scheme, and the encryption scheme corresponding to the learning model to be transmitted.

S24: The learning model receiving unit17of the apparatus10secures memory to receive the learning model, and notifies the maintenance server30of the preparation completion (preparation OK) of reception of the learning model. If sufficient memory size to allow the reception of the learning model is not secured, the learning model receiving unit17of the apparatus10requests the learning model transmitting unit32of the maintenance server30to divide and transmit the learning model.

S25: The learning model transmitting unit32of the maintenance server30transmits the learning model (the data) to the apparatus10.

S26: The learning model receiving unit17of the apparatus10determines from the size of the received data that the learning model is received, and notifies the maintenance server30that the reception of the learning model is completed. The apparatus10sets the received learning model in the detector13.

The maintenance server30and the apparatus10execute the learning model transmission processing described so far for the number of the service calls.

<Detection of Sign for Each Service Call>

FIG. 13is an example of an explanatory chart of a procedure of detecting the abnormality or the failure by using the learning model generated for each of the service calls and each of the paper jams.

The detector13provided in the apparatus10has a framework structure capable of handling the various learning models. The detector13that detects the certain service call or the certain type of the paper jam by setting the learning model data received from the maintenance server30can be implemented. The detector13is preferably prepared for each of the service call numbers and each of the paper jams to be detected. InFIG. 13, the learning models corresponding to the three service calls are set (S101).

The state monitoring unit16of the apparatus10inputs the apparatus monitoring data, which corresponds to the learning model of the service call, from the apparatus monitoring data collected in the normal operation to the detector (S102).

There is a case where the detector13detects the sign of the abnormality or the failure (S103). In such a case, the sign notifying unit18of the apparatus10transmits the sign detection result of the abnormality or the failure to the maintenance server30(S104). The sign detection result includes the service call number (including the event such as the paper jam).

FIG. 14is an example sequence diagram illustrating the operation of the maintenance system100at the time when the apparatus10predicts the abnormality or the failure.

S31: The detector13of the apparatus10predicts the abnormality or the failure. That is, in regard to the detector corresponding to any one of the service call, the probability of the occurrence of such a service call exceeds a threshold.

S32: The sign notifying unit18of the apparatus10transmits the sign detection result including the apparatus number and the service call number to the maintenance server30. In addition, the sign notifying unit18confirms whether the apparatus10holds information on when the maintenance work is performed last time upon the detection of the same service call number. Then, the sign notifying unit18inputs a date of the previous maintenance work in the sign detection result. As a result, it can be understood an interval of the occurrence of the same abnormality or the same failure.

Even in the case where the sign of the abnormality or the failure of the same service call number is detected in a period from the first sign detection to the maintenance work, the sign notifying unit18of the apparatus10does not notify the server30until the maintenance work is performed. In this way, repeated notification of the same service call can be prevented. For this determination, it is sufficient to refer to the service call number in which the maintenance completion date is not recorded in the sign detection record data22. Alternatively, even in the case where the sign detection result is notified from the apparatus10, the maintenance server30may not request the same maintenance work to a maintenance center50until the maintenance work is performed.

S33: The display control unit14of the apparatus10displays a sign detection screen501illustrated inFIG. 15on the operation panel411. On the sign detection screen501, a message indicating that the maintenance work is performed at a later date is displayed.FIG. 15is a view of an example of the sign detection screen501displayed on the operation panel411of the apparatus10. On the sign detection screen501, as an example, such a message502that “SIGN OF ABNORMALITY OR FAILURE IS DETECTED. SERVICE CALL NUMBER IS101. MAINTENANCE WORK WILL BE PERFORMED AT LATER DATE.” is displayed. The user reads the message502and can understand that the abnormality or the failure does not occur but the apparatus10is treated prior to the occurrence of the actual abnormality or the actual failure.

S34: Referring back toFIG. 14, the description will be made. The sign detection result recording unit19of the apparatus10records the sign detection result in the sign detection record data22. As a result, the prediction of the abnormality or the failure can be recorded before the abnormality or the failure actually occurs.

S35: Meanwhile, the sign receiving unit33of the maintenance server30receives the sign detection result and transmits the sign detection result to the maintenance content recording unit34. The maintenance content recording unit34records in the maintenance content data43that the abnormality or the failure is predicted.

S36: The service arranging unit36of the maintenance server30transmits a maintenance request including the apparatus number, customer information, and the service call number to the maintenance center50so as to arrange the maintenance work.

S37: According to the notification from the maintenance server30, a customer engineer9or the like adjusts a maintenance schedule with the customer of the apparatus10. As a result, the customer engineer9visits the customer and performs the maintenance of the apparatus10that is based on the service call number. For example, in the case of the abnormality or the failure, the customer engineer9replaces the component as the cause of the generation of the service call.

S38: When performing the maintenance, the customer engineer9records the performance of the maintenance in the apparatus10. That is, the maintenance completion date is recorded in association with the service call number in the sign detection record data22.

S39: The sign notifying unit18of the apparatus10detects recording of the maintenance completion date in the sign detection record data22, and transmits the maintenance performance notification including the apparatus number and the service call number to the maintenance server30.

S40: The sign receiving unit33of the maintenance server30transmits the maintenance performance notification to the maintenance content recording unit34. Thus, the maintenance content recording unit34associates the maintenance completion date with the apparatus number and the service call number and records the maintenance completion date in the maintenance content data43.

In this way, the maintenance server30collects the maintenance contents from the apparatuses10nationwide. Accordingly, the report generating unit37of the maintenance server30generates the report on the basis of the aggregated data of the service call numbers notified in a specific period (a week, a month, a quarter, or the like), and transmits the report to each of the maintenance work sites (the maintenance centers).

FIG. 16is a table of an example of the report generated by the report generating unit37. The report illustrated inFIG. 16is a monthly report issued monthly. The report has fields of the generated SC number, the number of calls, and a ratio, and a statistic of one month is recorded in each of the fields. When such a monthly report is graphed on a monthly basis, a trend such as an increasing trend or a reducing trend can be comprehended for each of the service calls.

As it has been described so far, the maintenance system100according to the present embodiment can predict the abnormality or the failure from the apparatus monitoring data prior to the occurrence of the abnormality or the failure. Thus, the customer engineer or the like can handle the abnormality or the failure in the periodic inspection, and the frequency of the urgent maintenance work can be reduced. In addition, the component and the like can be replaced before the abnormality or the failure occurs. Thus, the downtime that prevents the customer from using the apparatus10is less likely to occur.

In addition, the accuracy of the prediction model is gradually increased by the learning. Thus, the learning model for predicting the abnormality or the failure can be developed without being fixed to the preset detection condition of the abnormality and the failure. Furthermore, the load of the maintenance server30does not become extremely large.

Other Application Examples

The best mode for carrying out the present invention has been described so far by using the embodiment. However, the present invention is not limited to such an embodiment in any respect, and various modifications and replacement can be made to the embodiment within the scope that does not depart from the gist of the present invention.

For example, the description is made on the prediction of the abnormality or the failure of the apparatus such as the multifunction peripheral in the present embodiment. However, the abnormality or the failure of a machine tool such as a lathe may be predicted.

In the present embodiment, the log data is mainly the text data. However, at least one of internal sound data of the apparatus and the image data for which the inside of the apparatus is captured may be used as the log data.

Further, the method in the present embodiment is the same as or similar to so-called artificial intelligence (AI) and belongs to a field of such a technique. The method in the present embodiment can also be considered as a technique related to big data due to use of the large volume of the data.

The configuration examples illustratedFIG. 5and the like are each divided in accordance with the main functions in order to facilitate understanding of the processing by the apparatus10and the maintenance server30. The invention of the present application is not limited by a method for dividing of processing units and a name of each of the processing unit. The processing by each of the apparatus10and the maintenance server30can further be divided into more processing units in accordance with the processing contents. Alternatively, the processing can be divided such that each of the processing units includes more processing.