System and its control method

Abstract

To enable users to recognize the wear and tear calculation method used in a system that predicts the lifespan of consumable parts of a device. [Solution] The system for predicting the lifespan of consumable parts of device 102 includes: a parts information receiving unit 304 that receives data on consumable parts collected in device 102, including the degree of wear of the consumable parts and the degree of wear calculation means used to estimate the degree of wear; a parts life prediction unit 303 that predicts the lifespan of the consumable parts based on the degree of wear of the consumable parts included in the data on consumable parts collected from device 102; and a UI management unit 305 that generates and provides a UI that displays the predicted lifespan of the consumable parts along with information based on the type of degree of wear calculation means.

Description

Technical Field

[0001] The present invention relates to a system for predicting the life of consumable parts of a device and a control method therefor.

Background Art

[0002] An electrophotographic image forming apparatus (hereinafter referred to as a device) that forms a visible image by scanning charging, exposure, and development has consumable parts that need to be replaced regularly in addition to paper and toner. Specifically, there are a developing device, a drum, an ITB (intermediate transfer belt), a fixing device, a paper feed roller in the paper feed stage, and the like. If the life of these parts is exceeded, malfunctions such as image defects and errors will occur. Therefore, in recent years, a mechanism has been proposed to predict the life of consumable parts (the time of part replacement) and complete the replacement at the user's end before the life of the consumable parts is reached. Life prediction is performed by monitoring the degree of wear of consumable parts. The wear prediction method uses either a counter value based on the number of times the part has been used or a so-called life value calculated by the part itself based on its own logic. Generally, the wear degree based on the life value is more accurate. In recent years, a microdevice called a sensing device has been installed in consumable parts, and it has become possible to estimate the wear degree more accurately by measuring various information during device operation. Furthermore, there are cases where the logic of wear degree estimation itself is improved by upgrading the device. Patent Document 1 discloses a method for determining the priority based on the prediction history of whether to use the life value or the counter value for each part.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, while Patent Document 1 uses either a life value or a counter value to estimate the lifespan of a component, it is not possible for the system user to recognize which value was used for the calculation. For customer engineers who are system users, knowing how the lifespan estimation was performed is directly related to the accuracy of the component's lifespan estimation and is therefore important. If it is not possible to understand what kind of wear estimate was used as the basis for the remaining lifespan prediction on the system, it becomes difficult to determine the appropriate replacement time for the component. Therefore, it is necessary to enable system users to understand what kind of wear estimate was used as the basis for the remaining lifespan prediction on the system.

[0005] The present invention aims to enable users to recognize the wear and tear calculation means used in a system for predicting the lifespan of consumable parts of a device. [Means for solving the problem]

[0006] To solve the above problems, the present invention provides a system for predicting the lifespan of consumable parts of a device, comprising: a receiving unit that receives data on consumable parts collected in the device, including the degree of wear of the consumable parts and a wear degree calculation means used to estimate the degree of wear; a lifespan prediction unit that predicts the lifespan of the consumable parts based on the degree of wear of the consumable parts included in the data on consumable parts collected from the device; and a UI management unit that generates and provides a UI that displays the predicted lifespan of the consumable parts along with information based on the type of the wear degree calculation means. [Effects of the Invention]

[0007] According to the present invention, a user can recognize the wear degree calculation means used in a system for predicting the lifespan of consumable parts of a device. [Brief explanation of the drawing]

[0008] [Figure 1] This is a diagram showing the overall system configuration. [Figure 2] This diagram shows the hardware configuration of the component life prediction server 101. [Figure 3] This figure shows the hardware configuration of device 102. [Figure 4] This is a diagram showing the system's software configuration. [Figure 5] This flowchart shows the process for estimating the degree of wear of consumable parts. [Figure 6] This figure shows an example of a selection table for the wear and tear estimation method. [Figure 7] This is a flowchart showing the process for updating the remaining lifespan of a component in days. [Figure 8] This is a diagram illustrating the notification information regarding wear and tear levels. [Figure 9] This is a diagram illustrating the component information. [Figure 10] This figure shows an example of a web application screen. [Figure 11] This is a diagram explaining the device information. [Modes for carrying out the invention]

[0009] (Example 1) Figure 1 shows the overall system configuration. The system in this embodiment is a system for predicting the lifespan of consumable parts of a device. The system includes a parts lifespan prediction server 101 and a device 102, which are connected to each other via a network 103 so that they can communicate with one another. In addition to the parts lifespan prediction server 101 and the device 102, the system may also include servers that provide other functions and services.

[0010] Device 102 is a network device that uses consumable parts. There may be multiple devices 102 managed by the system. Device 102 is, for example, a multifunction peripheral (MFP) image processing device that integrates multiple functions such as printing, reading, and faxing. Note that device 102 is not limited to an MFP, but may be any image processing device that has any of the following functions: printing, copying, scanning, data network transmission, or faxing, such as a printer, scanner, or 3D printer.

[0011] In this embodiment, device 102 collects data on consumable parts within the device based on a request from the component life prediction server 101 and notifies the component life prediction server 101. When collecting data on consumable parts, device 102 also performs a process to estimate the degree of wear of each consumable part. In the process of estimating the degree of wear of consumable parts, a wear degree estimation algorithm (wear degree estimation means), which is an algorithm for estimating the degree of wear, is used. The data on consumable parts that device 102 notifies the component life prediction server 101 includes the degree of wear calculated by the estimation process and information indicating the wear degree estimation algorithm used to estimate the degree of wear.

[0012] The component life prediction server 101 predicts the remaining number of days until the lifespan of the consumable parts of device 102. More specifically, the component life prediction server 101 collects the degree of wear of the consumable parts of device 102 as time-series data, calculates the progress of wear (hereinafter referred to as wear progress), and calculates the remaining number of days until the lifespan from the wear progress and the degree of wear of the consumable parts. The wear progress is notified from device 102 to the component life prediction server 101. In this embodiment, device 102 and the component life prediction server 101 communicate directly, but a server other than the component life prediction server 101 may be involved. The component life prediction server 101 may be composed of a single information processing device, or it may be composed of a group of devices using cloud computing technology. That is, the component life prediction server 101 may be realized by one or more information processing devices, a virtual machine (cloud service) that utilizes resources provided by a data center including information processing devices, or a combination of these.

[0013] The component life prediction server 101 and the device 102 are connected via a network 103. Network 103 is a communication network implemented by either a LAN (Local Area Network), a WAN (Wide Area Network), or a combination thereof, such as the Internet. Network 103 only needs to be configured to enable data transmission and reception, and the communication method is not limited. For example, network 103 can consist of any of the following: LAN, WAN, cellular network such as LTE or 5G, wireless network, telephone line, dedicated digital line, or a combination thereof.

[0014] Figure 2 shows the hardware configuration of the component life prediction server 101. The component life prediction server 101 includes a CPU 201, memory 202, HDD 203, input control unit 205, input device 206, display control unit 207, display device 208, and network control unit 209. The CPU 201, memory 202, HDD 203, input control unit 205, display control unit 207, and network control unit 209 are connected to the system bus 204.

[0015] The CPU (Central Processing Unit) 201 controls the entire component life prediction server 101 by comprehensively controlling each hardware connected to the system bus 204. The memory 202 functions as the main memory, work area, etc. of the CPU 201. The HDD 203 (Hard Disk Drive) is a large-capacity storage device having a non-volatile storage area. The HDD 203 stores an OS (Operating System), data, software programs, etc. In this embodiment, the case where the non-volatile large-capacity storage device is an HDD is described as an example, but it is not limited thereto. For example, the large-capacity storage device may be an SSD (Solid State Drive) or the like. The CPU 201 executes various control processes by expanding and executing the program stored in the memory 202 or the HDD 203 in the memory 252.

[0016] The input device 206 receives operations from the user. The input device 206 is, for example, a keyboard, a pointing device, or the like. The input control unit 205 controls the input from the input device 206. The display device 208 performs display for the user. The display device 208 is, for example, a display such as a liquid crystal screen. The display control unit 207 controls the display on the display device 208. Note that the input device 206 and the display device 208 may be realized by an integrated touch panel. By associating the input coordinates and the display coordinates on the touch panel, a graphical user interface (GUI) can be configured as if the user can directly operate the screen displayed on the touch panel. The network control unit 209 transmits and receives data bidirectionally with an external device via the network 103. In this embodiment, the network control unit 209 communicates with the device 102 via the network 103.

[0017] FIG. 3 is a diagram showing the hardware configuration of device 102. The device 102, which is an image processing apparatus, includes a main board 250, an input device 256, a display device 258, an engine 270, a developing unit 276, a drum 278, an ITB 280, a fixing unit 282, and a paper feed stage 284. The main board 250 and the engine 270 are connected by engine I / Fs 260 and 274 that they each have.

[0018] The main board 250 receives an output instruction, converts it into data that can be processed by the engine 270, and transmits it to the engine 270 as an output instruction. The engine 270 that has received the instruction output from the main board 250 executes processing based on the instruction. For example, in the case of printing, the main board 250 receives PDL (Printer Description Language) data as a print instruction from the information processing apparatus 101. Then, the main board 250 converts the received PDL data into bitmap data for printing and transmits it to the engine 270.

[0019] The main board 250 includes a CPU 251, a memory 252, an HDD 253, an input control unit 255, a display control unit 257, a network control unit 259, and an engine I / F 260. The CPU 251, the memory 252, the HDD 253, the input control unit 255, the display control unit 257, the network control unit 259, and the engine I / F 260 are interconnected by a system bus 254. The CPU 251 controls the main board 250 by comprehensively controlling each hardware connected to the system bus 254. The memory 252 functions as the main memory, work area, etc. of the CPU 251. The HDD 253 is a non-volatile storage area and stores data, programs, etc. as a large-capacity storage device. The memory 252 or the HDD 253 stores controller firmware. The CPU 251 realizes various controls by expanding and executing the controller firmware (program) stored in the memory 252 or the HDD 253 in the memory 252.

[0020] The input device 256 accepts user input. The input device 256 is, for example, a keyboard or a pointing device. The input control unit 255 controls the input from the input device 256. The display device 258 displays information to the user. The display device 258 is, for example, a display such as an LCD screen. The display control unit 257 controls the display on the display device 258. The input device 256 and the display device 258 may be implemented as an integrated touch panel. The network control unit 259 sends and receives data bidirectionally with external devices via the network 103. In this embodiment, the network control unit 259 communicates with the information processing device 101 via the network 103. The engine I / F 260 connects the main board 250 and the engine 270.

[0021] The engine 270 includes a CPU 271, memory 272, engine I / F 274, developing machine control unit 275, drum control unit 277, ITB control unit 279, fuser control unit 281, and paper feed tray control unit 283, which are interconnected by a system bus 273. The CPU 271 controls the engine 270 by comprehensively controlling each piece of hardware connected to the system bus 273. The memory 272 functions as the main memory and work area of ​​the CPU 271. The memory 272 stores the engine firmware. The CPU 271 performs various controls by loading the engine firmware (program) stored in the memory 272 onto the memory 272 and executing it. The engine I / F 274 connects the engine 270 and the main board 250 via the engine I / F 260.

[0022] The developing unit 276 receives instructions from the engine firmware via the developing unit control unit 275 and develops the image to be output on the drum 278 using toner (not shown) based on the received instructions. The developing unit control unit 275 controls the developing unit 276. The drum 278 is charged by a charger (not shown) and then exposed by an exposure device (not shown) to form an electrostatic latent image on the drum 278 corresponding to the output image. After the electrostatic latent image is formed by the drum 278, the image is developed by the developing unit 276. There is a drum 278 for each color: cyan, magenta, yellow, and black. The electrostatic latent image formation process is performed for each color and superimposed on the ITB control unit 279 to form a color image. In this embodiment, the same component (drum 278) is used for cyan, magenta, yellow, and black as an example, but different components may be used for each color. The drum control unit 277 controls the drum 278.

[0023] The ITB280 receives instructions from the engine firmware via the ITB control unit 279 and transfers the toner on the drum 278 to the ITB280 based on the received instructions. The ITB control unit 279 controls the ITB280. The fuser 281 fixes the toner transferred to the ITB280 onto the paper as a printed image by applying high temperature and high pressure. The fuser control unit 281 controls the fuser 281. The paper feed stage 284 stores the paper to be used for printing. The paper feed stage 284 receives instructions from the engine firmware via the paper feed stage control unit 283 and feeds the paper to be used for printing based on the received instructions. The paper feed stage 284 is equipped with paper feed rollers (not shown) and transports the paper using the paper feed rollers. The paper feed stage control unit 283 controls the paper feed stage 284.

[0024] The developing unit 276, drum 278, ITB 250, fuser 282, and paper feed stage 284 are examples of components that perform output processing for device 102. These components are consumable parts that wear out as a result of the output processing (printing) of device 102 and require replacement according to the lifespan of each component. The consumable parts are detachable from device 102 and can be replaced on a per-component basis. In other words, the developing unit 276, drum 278, ITB 250, fuser 282, and paper feed stage 284 are consumable parts of device 102. The developing unit control unit 275, drum control unit 277, ITB control unit 279, fuser control unit 281, and paper feed stage control unit 283 are control units for the consumable parts. Each consumable part and its control unit communicate with each other, for example, via wired communication such as GPIO (General-Purpose Input / Output) or proximity wireless communication such as RFID (Radio Frequency Identifier). Device 102 may also have consumable parts other than the developing unit 276, drum 278, ITB 250, fuser 282, and paper feed stage 284 described in this embodiment.

[0025] The degree of wear of each consumable component varies depending on the usage of device 102. Therefore, by replacing only some components that have reached the end of their lifespan, device 102 can be used continuously without needing to replace the device 102 itself. In addition, each consumable component is equipped with a memory tag (not shown). Usage information is stored in the memory tag, which indicates the component serial number for identifying the consumable component and the degree of wear of the consumable component. The usage information on the memory tag can be read by the control unit of the consumable component via a memory tag interface (not shown).

[0026] Figure 4 shows the software configuration of the system. First, the software configuration of the component life prediction server 101 will be described. The program that implements the software configuration of the component life prediction server 101 is read from memory (memory 202, HDD 203) and executed by the CPU 201. The component life prediction server 101 has a device management unit 301, a component information management unit 302, a component life prediction unit 303, a component information receiving unit 304, a UI management unit 305, and a notification unit 306.

[0027] The device management unit 301 manages device information for which component lifespan is to be determined by the component lifespan prediction server 101. Target devices are added or deleted by device registration or deletion instructions to the component lifespan prediction server 101. Alternatively, the configuration may target devices that are managed by a separate server that comprehensively manages device information and have license information indicating whether the component lifespan prediction function by the component lifespan prediction server 101 is enabled or not. In this embodiment, device 102 is the target device.

[0028] Furthermore, the device management unit 301 sends a data collection request to the device 102. The data collection request includes a request for notification of the wear level of consumable parts of the device 102. The wear level notification request is an instruction to periodically notify the component life prediction server 101 of the wear level of consumable parts within the device. The data collection request may also include other data collection instructions, such as information on the replacement of consumable parts. The component information receiving unit 304 receives the wear level notification of consumable parts, etc., notified by the device 102 that has received the wear level notification request.

[0029] The component information management unit 302 manages component information for the target device on the component life prediction server 101. The component information includes key information for identifying the component and its mounting location, the degree of wear of the component, the number of days remaining until replacement calculated by life prediction, and the wear progress indicating the rate of wear. The component information also includes information indicating the wear estimation means (wear estimation algorithm) used to estimate the degree of wear of the component. In this embodiment, the component information management unit 302 manages the component information of device 102. The component information will be explained using Figure 9.

[0030] The component life prediction unit 303 predicts the lifespan of consumable parts based on the degree of wear included in the data of consumable parts of device 102 received by the component life reception unit 304. The lifespan is calculated as the number of days remaining until the part needs to be replaced. More specifically, the component life prediction unit 303 calculates the lifespan of consumable parts based on the degree of wear included in the data of consumable parts and the wear progress held by the component information management unit 302. When calculating the lifespan of consumable parts, if there is learning data for lifespan prediction, the component life prediction unit 303 predicts the lifespan of consumable parts based on the wear degree of the learning data. The component life prediction unit 303 notifies the component information management unit 302 of the calculated remaining days for the consumable part, the latest degree of wear, and the updated wear progress. The component information management unit 302, upon receiving notification from the component life prediction unit 303, stores the remaining days for the consumable part, the latest degree of wear, and the updated wear progress as component information.

[0031] The UI management unit 305 generates and provides the UI (user interface) provided by the component life prediction server 101. The functions provided by the component life prediction server 101 are implemented, for example, as a web application. The UI management unit 305 displays the UI provided by the component life prediction server 101 on the web browser of a terminal (not shown) that communicates with the component life prediction server 101. The terminal may be device 102, or a network device such as a tablet, smartphone, or PC owned by a system user. In this implementation, the UI provided by the UI management unit 305 is a screen for checking the condition (status) of device 102. The UI displays condition information (information representing the status of the device). The condition information includes component information of device 102. The component information displays the predicted lifespan of consumable parts, along with information based on the type of wear degree calculation means. Note that the component information may also include key information for identifying the part and its mounting location, the degree of wear of the part, and the wear rate indicating the progress of wear.

[0032] The web application consists of HTML to represent web pages, CSS (Cascading Style Sheets) for style sheets, and a programming language (such as JavaScript) that runs on a web browser. The component information displayed on the web application is obtained via an API provided by the component information management unit 302 using JavaScript that runs on a web browser. The web application provided by the component life prediction server 101 can be displayed and executed on any network node that can connect to the component life prediction server 101 via a web browser. Customer engineers and their managers responsible for maintenance of device 102 and arranging the delivery of consumable parts can check the UI provided by the component life prediction server 101 online (via a web browser). The notification unit 306 issues various notifications to system users. The notification unit 306 communicates directly or indirectly with, for example, a mail server (not shown) or a cloud computing system (not shown).

[0033] Next, the software configuration of device 102 will be described. The program that implements the software configuration of the main board 250 of device 102 is read from memory 252 or HDD 253 and executed by CPU 251. The program that implements the software configuration of the engine 270 of device 102 is read from memory 272 and executed by CPU 271. Device 102 includes a data acquisition request receiving unit 311, a data acquisition unit 312, and a data acquisition notification unit 313.

[0034] The data collection request receiving unit 311 receives a data collection request from the component life prediction server 101 and notifies the data collection unit 312 of the received data to be collected. The data collection unit 312 holds the data to be collected that was notified by the data collection request receiving unit 311 and collects various data within the device 102 based on the data to be collected. The data collection unit 312 transmits the collected data to the data collection notification unit 313. The data collection notification unit 313 notifies the component life prediction server 101 of the data collected by the data collection unit 312.

[0035] In this embodiment, the data collection request receiving unit 311 receives a data collection request for the degree of wear of consumable parts from the component life prediction server 101. Based on the data collection request for the degree of wear of parts, the data collection request receiving unit 311 notifies the data collection unit 312 of the degree of wear of consumable parts in the device to be collected. The data collection unit 312 periodically collects the degree of wear of consumable parts in the device. The data collection unit 312 transmits the collected degree of wear of consumable parts to the data collection notification unit 313. The data collection notification unit 313 transmits the degree of wear of consumable parts to the component life prediction server 101, which is the source of the data collection request for the degree of wear of consumable parts. Note that there may be other servers connected to the component life prediction server 101 and device 102, and the server that issues the data collection request and the server that receives the collected data may be physically different devices.

[0036] Here, the method for estimating the degree of wear of consumable parts within the device will be explained with reference to Figure 5. Figure 5 is a flowchart showing the process for estimating the degree of wear of consumable parts. The process for estimating the degree of wear of consumable parts is performed in device 102. Each process performed by device 102 in Figure 5 is realized by the CPU 271 reading and executing a program or the like stored in memory 272. The process for estimating the degree of wear of consumable parts is performed periodically, for example, based on a periodic collection request included in the data collection request for the degree of wear of consumable parts from the parts life prediction server 101. For example, the process for estimating the degree of wear of consumable parts is periodically started every 12 hours by a timer device (not shown) in device 102. The process for estimating the degree of wear of consumable parts may also be performed irregularly, triggered by user instructions from the input device 256 of device 102 or by some device operation such as device startup.

[0037] This process is initiated, for example, by the firing of a timer set to occur every 12 hours. In step S401, the data acquisition unit 312 creates a list of unprocessed consumable parts in memory 272 and writes all consumable parts mounted on device 102 to the list of unprocessed consumable parts. The fact that all consumable parts are written to the list of unprocessed consumable parts means that the wear estimation process has not been performed for all consumable parts.

[0038] In step S402, the data collection unit 312 refers to the created list of unprocessed consumable parts and enters iterative processing for the unprocessed consumable parts. The processing in steps S403 to S408 is repeatedly executed for the unprocessed consumable parts. In step S402, the data collection unit 312 checks if any unprocessed consumable parts exist. If any unprocessed consumable parts exist, the data collection unit 312 proceeds to the wear degree estimation process. On the other hand, if no unprocessed consumable parts exist, i.e., if the wear degree estimation process for all consumable parts has been completed, the data collection unit 312 terminates this process.

[0039] As part of the wear estimation process, steps S403 to S408 are performed on unprocessed consumable parts. In step S403, the data acquisition unit 312 selects one consumable part from the list of unprocessed consumable parts. In step S404, the data acquisition unit 312 obtains a wear estimation means (consumable part estimation method) corresponding to the selected consumable part. The wear estimation means is a wear estimation algorithm used by the data acquisition unit 312 to estimate wear. Here, the wear estimation means referenced in step S404 will be explained using Figure 6.

[0040] Figure 6 shows an example of a wear-to-use estimation means selection table. The wear-to-use estimation means selection table 500 consists of columns such as part type, presence or absence of a sensing device, type of wear-to-use estimation algorithm, and algorithm version. For example, the wear-to-use estimation algorithm (consumable part estimation means) is uniquely determined according to the part type and the presence or absence of a sensing device. In Figure 6, part types are listed by name, but in practice, a type code indicating the type may be used. Also, the listed parts are just examples and are not limited to these. For example, drums may be treated the same for all colors, or they may be managed separately by toner color. A sensing device is a microdevice capable of measuring information such as the current value, temperature, and rotation speed flowing through each consumable part. In recent years, some consumable parts have been equipped with sensing devices, making it possible to estimate the wear-to-use degree with higher accuracy that reflects the actual state of the consumable part.

[0041] In this embodiment, the types of wear estimation algorithms include counter-based algorithms, rule-based algorithms, and machine learning models. The counter-based algorithm estimates the degree of wear by dividing counter information for each consumable part by a design target value. Counter information is, for example, the value counted for each part. For example, for a paper feed roller, the number of printed pages is the counter information. The rule-based algorithm estimates the degree of wear by applying predetermined rules to the counter information. That is, the rule-based algorithm is based on counter information, but instead of simple division like the counter-based algorithm, it performs predetermined processing on the counter information to estimate the degree of wear. The machine learning model estimates the degree of wear by inputting sensing information acquired from a sensing device into a pre-built machine learning model. Therefore, the machine learning model is a means of estimating the degree of wear that can be used when a sensing device that measures consumable parts is installed. In this embodiment, the machine learning model is treated as having the highest accuracy in estimating the degree of wear, followed by the rule-based algorithm, and then the counter-based algorithm, in that order of decreasing accuracy. The data acquisition unit 312, when there are multiple wear estimation algorithms available for a component whose wear is to be estimated, selects, for example, the wear estimation algorithm with the highest estimation accuracy. These estimation algorithms are examples only and are not limited to these. The algorithm version is a numerical value assigned to identify the wear estimation algorithm and its version for each component type. The algorithm version allows for the determination of the type and version of the wear estimation algorithm (wear calculation means). For example, a higher algorithm version number indicates a newer wear estimation algorithm, suggesting that more accurate wear estimation is possible.

[0042] The wear rate estimation means selection table 500 may be updated along with the firmware update of device 102. This allows for the addition of estimation algorithms for components that support the mounting of new sensing devices, or updates to improve the accuracy of existing algorithms. In the example shown in Figure 6, the wear rate estimation algorithm for the drum, a consumable component, was originally only a rule-based algorithm, but a machine learning model was later added. Therefore, there are two types of wear rate estimation algorithms for the drum: a rule-based algorithm and a machine learning model. In Figure 6, the algorithm version of the rule-based algorithm for the drum is 1.0. For example, if the rule-based algorithm for the drum is updated (upgraded), the algorithm version becomes 1.1. In Figure 6, the algorithm version of the machine learning model for the drum is 2.0.

[0043] Let's return to the explanation of Figure 5. In step S403, let's assume that the data acquisition unit 312 has selected a drum unit (for K toner). In step S404, the data acquisition unit 312 acquires a wear rate estimation means (wear rate estimation algorithm). To select the wear rate estimation means (wear rate estimation algorithm) to acquire, the data acquisition unit 312 first checks whether or not a sensing device is installed. The data acquisition unit 312 obtains whether or not the drum unit in question is equipped with a sensing device via the drum control unit 277. In this embodiment, we will continue the explanation assuming that the drum unit is equipped with a sensing device. The data acquisition unit 312 refers to the wear rate estimation means selection table 500 and obtains algorithm version 2.0 of a machine learning model that can be selected when a sensing device is present as a wear rate estimation means (wear rate estimation algorithm) for estimating the wear rate of the drum unit. If the drum unit is not equipped with a sensing device, the data acquisition unit 312 will select a rule-based algorithm as the wear rate estimation means. Thus, when determining the means for estimating wear and tear, it is necessary to check whether or not there is a sensing device to measure the wear parts. If a sensing device is available, a machine learning model is selected; if there is no sensing device, a rule-based algorithm is selected.

[0044] In step S405, the data acquisition unit 312 acquires parameters related to the consumable parts necessary for executing the wear-to-wear estimation means (wear-to-wear estimation algorithm) determined in step S404. The parameters necessary for executing the wear-to-wear estimation algorithm include counter information corresponding to the consumable parts and sensing data collected from sensing devices attached to the consumable parts. Note that the parameters related to the consumable parts are not limited to counter information and sensing data.

[0045] In step S406, the data collection unit 312 estimates the degree of wear using the wear estimation means (wear estimation algorithm) determined in step S404. In step S407, the data collection unit 312 acquires the current date and time information. In step S408, the data collection unit 312 links the degree of wear estimated in step S406, the wear estimation means (wear estimation algorithm) used in the estimation in step S406, and the date and time information acquired in step S407, and stores them in the HDD 253. The algorithm version corresponding to the wear estimation algorithm is stored as information about the wear estimation means (wear estimation algorithm) used in the estimation in step S406. For example, if a machine learning model is used to estimate the wear of the drum, algorithm version 2.0 is stored as information about the wear estimation means (wear estimation algorithm). In step S409, the data collection unit 312 deletes the consumable parts that were the subject of the wear estimation process selected in step S403 from the list of unprocessed consumable parts. Then, the data acquisition unit 312 returns to step S402 to check if there are any unprocessed consumable parts.

[0046] In this way, by repeating the wear estimation process for each consumable part, the data acquisition unit 312 of device 102 estimates the wear level of each consumable part. Subsequently, the wear level estimated by the data acquisition unit 312 for each consumable part through the wear estimation process (Figure 5) is transmitted to the component life prediction server 101 by the data acquisition notification unit 313. More specifically, in step S408, the data acquisition notification unit 313 acquires information linked to the estimated wear level stored in the HDD 253, the wear level estimation means (wear level estimation algorithm) used for the estimation, and the date and time information when the estimation process was performed. Then, the data acquisition notification unit 313 notifies the component life prediction server 101 of the information linked to the estimated wear level of each consumable part, the wear level estimation means used for estimating the wear level of each consumable part, and the date and time information when the estimation process was performed as wear level notification information for consumable parts. The wear level notification information will be described later using Figure 8.

[0047] Next, we will describe the process by which the component life prediction server 101 calculates the remaining lifespan of the consumable parts of device 102 and updates the remaining lifespan of the consumable parts. Figure 7 is a flowchart showing the process of updating the remaining lifespan of the parts. Each process performed by the component life prediction server 101 is stored as executable program code in the HDD 203 of the component life prediction server 101, loaded into memory 202 by the CPU 201, and executed.

[0048] This process is triggered when the component information receiving unit 304 of the component life prediction server 101 receives a notification of the wear level of consumable parts from the data collection notification unit 313 of device 102. The information included in the wear level notification of consumable parts of device 102 received by the component information receiving unit 304 is managed by the component information management unit 302. Alternatively, the process may be performed at various timings, for example, by storing received information from certain devices 102 in the component information receiving unit 304 and executing the process periodically according to a specific schedule within the component life prediction server 101.

[0049] In step S601, the component life prediction unit 303 refers to the wear level notification information received from device 102. Here, the notification information is information that links the estimated wear level of each consumable component, the wear level estimation means used to estimate the wear level of each consumable component, and the date and time information on when the estimation process was performed. Specifically, the notification information is text information as shown in Figure 8.

[0050] Figure 8 shows an example of notification information regarding the wear and tear of consumable parts. The notification information 700 includes the notification event type 701, device-specific ID 702, event occurrence date and time 703, and part list 704. The part list 704 includes part wear and tear information 705 for each consumable part. The part wear and tear information 705 includes the part code 706, part serial number 707, wear and tear level 708, and wear and tear estimation algorithm version 709. The event type 701 contains a value indicating that it is a notification event regarding the wear and tear of a consumable part. The device-specific ID 702 is information that uniquely identifies device 102. The event occurrence date and time 703 is the date and time obtained in step S407. The part code 706 is the type of part. The part serial number 707 is information that uniquely identifies the part. The wear and tear level 708 is the wear and tear of the part estimated in step S406. The wear and tear estimation algorithm version 709 is information indicating the wear and tear estimation means (wear and tear estimation algorithm) used in the estimation in step S406.

[0051] In step S602, the component life prediction unit 303 refers to the component list 704 in the notification information 700 and enters a loop for unprocessed components (component wear information 705). The process of updating the remaining life days of consumable components in steps S603 to S605 is repeatedly executed for the unprocessed component wear information 705. In step S602, the component life prediction unit 303 checks if unprocessed component wear information exists in the component list 704. If unprocessed component wear information exists, the component life prediction unit 303 selects one component wear information to be processed and proceeds to the process of updating the remaining life days of consumable components. On the other hand, if no unprocessed component wear information exists, i.e., if the process of updating the remaining life days of consumable components for all component wear information in the component list 704 has been completed, the component life prediction unit 303 terminates this process. In the first step S602, the component life prediction unit 303 selects the component wear information 705, which is the top component wear information in the component list 704 within the notification information 700, as the component wear information to be processed.

[0052] In step S603, the component life prediction unit 303 obtains component information corresponding to the component in the component wear information selected as the processing target in S602 from the component information management unit 302. The component information obtained here is managed by the component information management unit 302 of the component life prediction server 101. The component life prediction unit 303 requests the component information management unit 302 to search for component information using the component serial number 707 included in the component wear information to be processed as the key. The component information management unit 302 searches for and obtains the component information corresponding to the component serial number 707 and sends it to the component life prediction unit 303. As a result, the component life prediction unit 303 can obtain component information managed by the component life prediction server 101 that corresponds to the component wear information to be processed from the component information management unit 302. Here, the component information will be explained using Figure 9.

[0053] Figure 9 shows an example of a component information table. Figure 9 shows component information for a single component. The component information table 800 consists of pairs of items and corresponding values. For example, part_serial is an item that indicates the component serial number, and device_id is an item that indicates the device-specific ID on which it is mounted. In the example shown in Figure 9, the component serial number is recorded as axy112233, and the device-specific ID on which it is mounted is recorded as f41549de-f036-4c7c-ae62-51390a8da83a.

[0054] `part_number` indicates the part number. `life_a_day` indicates the lifespan progress per day. `life_full` indicates the expected lifespan of the part. `life_days` indicates the number of days until the end of its lifespan. `part_code` indicates the part code. `life_progress` indicates the wear progress. `life_version` indicates the algorithm version of the wear estimation algorithm. `initial_learning` is a flag indicating that initial learning is in progress. `learning_start_date` indicates the learning start date.

[0055] Returning to the explanation of Figure 7, in step S604, the component life prediction unit 303 calculates the remaining lifespan in days for the component to be processed. For example, the component life prediction unit 303 calculates the component lifespan from the wear progress (life_progress) included in the component information 800 acquired in S603 and the wear degree 708 in the notification component information 705, and sets the difference between this and the separately set recommended replacement lifespan as the remaining lifespan in days. Note that general methods (moving average method, exponential smoothing method, ARIMA, neural network method, etc.) can be used to predict the lifespan, and the method for predicting the lifespan is not particularly limited.

[0056] Here, we will explain a specific example of the remaining lifespan calculation process. As an example, let's assume the expected lifespan of the component is 10,000 and the predictive coefficient, which can be set by the system user, is 1.4. The lifespan value of 14,000, obtained by multiplying the expected lifespan of the component by the predictive coefficient, is the component's lifespan. The learning data for this embodiment is assumed to contain information such as the trend of daily changes in wear and tear (the increase in wear and tear that has been leveled out by taking into account cycles such as holidays) from the wear and tear trend over the past 60 days. Here, we assume that it increases by 50 each day. Since the current wear and tear progress is 4,000, if the remaining 10,000 until the end of life decreases by 50 each day, the number of days until the end of life can be calculated to be 200 days.

[0057] In this embodiment, a fixed remaining lifespan calculation process is applied, but a different process may be applied depending on the wear rate estimation algorithm version 709 in the notification information 700. For example, if the wear rate estimation is a counter-based algorithm, the wear rate simply increases, but if it is derived from sensing data, the wear rate may decrease slightly in the short term, and a remaining lifespan calculation process that takes such data trends into account may be applied.

[0058] In step S605, the component life prediction unit 303 instructs the component information management unit 302 to update the component information. At this time, the wear rate estimation algorithm version 709 in the notification information 700 notified from device 102 is stored as life_version=2.0. In addition, the values ​​of each item of the other updated component information are updated and stored in the same manner.

[0059] The process of updating the remaining lifespan of consumable parts in steps S603 to S605 calculates the remaining lifespan for one consumable part, and the process returns to step S602 via step S606. The process of calculating and updating the remaining lifespan for unprocessed parts is then repeated. Once the remaining lifespan update process has been performed for all consumable parts, this flow terminates.

[0060] Figure 10 shows an example of a component information display screen. The component information display screen 900 is a web application screen provided by the UI management unit 305 of the component life prediction server 101. The UI management unit 305 generates the component information display screen 900 based on device information managed by the device management unit 301 and component information managed by the component information management unit 302, and provides it to the system user's terminal via a web browser. When the component life prediction server 101 detects that a device has been selected by the user on the web application's menu screen, etc., it provides the component information display screen 900 for the selected device.

[0061] The component information display screen 900 includes a device information display area 901 and a component information display area 902. The device information display area 901 displays device information such as the model name and serial number of the selected device. The component information display area 902 displays, for example, the component code, component number, component name, wear level, and remaining days of the components installed in the device in a list format. In the example shown in Figure 10, drums (K, C, M, Y) and ITB units are displayed. Note that the component information is not limited to these; other items such as the total number of sheets passed may also be displayed.

[0062] The UI management unit 305 obtains information to be displayed in the device information display area 901 from the device management unit 301. An example of device information obtained from the device management unit 301 is shown in Figure 11. Figure 11 is a diagram showing an example of a device information table. In the device information 1000, for example, the model name (model_name) and serial number (device_serial) are managed in association with the device-specific ID (device_id). The UI management unit 305 displays the model name and device-specific ID in the device information display area 901.

[0063] The UI management unit 305 obtains information to be displayed in the part information display area 902 from the part information management unit 302. The information obtained from the part information display area 902 is the part information shown in Figure 9. The UI management unit 305 displays the part code, part number, part name, wear level, and remaining days in the part information display area 902. In addition, when displaying the remaining days, the UI management unit 305 in this embodiment displays information that allows the user to recognize the accuracy of the remaining days estimation. For example, the UI management unit 305 displays information based on the type of wear level calculation means so that the user can recognize the accuracy of the estimation. In this embodiment, the UI management unit 305 highlights the use of a highly accurate wear level estimation means (wear level estimation algorithm) in the wear level estimation, thereby allowing the user to identify the accuracy of the remaining days estimation.

[0064] Here, we explain the need to display information based on the type of wear and tear calculation method so that system users can recognize the accuracy of the estimated remaining days along with the lifespan of consumable parts. Updating the wear and tear estimation method (wear and tear estimation algorithm) used to estimate wear and tear is done, for example, through a device firmware update. However, since firmware updates are performed on a per-device basis, the old and new wear and tear estimation methods will coexist among the many devices that customer engineers are responsible for maintaining. As the wear and tear estimation method is updated and the accuracy of wear and tear estimation becomes higher, the accuracy of predicting the lifespan of parts also becomes higher. As a result, the UI provided by the part lifespan prediction server 101 displays lifespan predictions with different accuracy for each device and part. In addition, it is desirable to replace consumable parts before they reach the end of their lifespan, but the error in the period during which replacement is recommended based on the lifespan (recommended replacement date) differs depending on whether the old or new wear and tear estimation method is used. If the lifespan is calculated based on the old wear and tear estimation method, which has lower accuracy than the new wear and tear estimation method, the error will be larger, and it is recommended to replace the part earlier within the error range to prevent failure. On the other hand, when the lifespan is calculated based on the new wear rate estimation method, the error is small, so replacing parts at the replacement date recommended by the old wear rate estimation method may result in replacing parts too early. Thus, if customer engineers, who are users of the system, cannot recognize whether the lifespan prediction is based on the wear rate calculated by the old or new wear rate estimation method, it becomes difficult to determine the appropriate replacement time for parts. Therefore, in this embodiment, information based on the wear rate estimation method is displayed on the UI as information indicating the accuracy of the remaining days estimate, along with the lifespan of consumable parts, to inform system users.

[0065] For example, the UI management unit 305 displays a mark such as "*" before the remaining days if the remaining days are based on a more accurate wear and tear estimation using data from sensing devices. In the example shown in Figure 10, for drum unit parts where the life_version estimated using a machine learning model is 2.0, a "*" is displayed before the remaining days to indicate that the remaining days are based on a more accurate wear and tear estimation. On the other hand, for ITBs where the life_version estimated using a conventional rule-based algorithm is 1.0, no mark indicating that the remaining days are based on a more accurate wear and tear estimation is displayed. Thus, when the algorithm version is 2.0, a mark indicating that the remaining days are based on a more accurate wear and tear estimation is displayed, while when the algorithm version is 1.0, the same mark is not displayed. It is possible to pre-configure which algorithm versions enable more accurate wear and tear estimation and for which marks indicating that the remaining days are based on a more accurate wear and tear estimation are displayed. This allows for a display indicating that the lifespan prediction is highly accurate when the wear and tear calculation means used to estimate wear and tear for lifespan prediction is a newer version than a predetermined version. Furthermore, if the numerical value is larger than the previous algorithm version, or if the units digit is larger, a mark indicating that the remaining days are based on a more accurate wear and tear estimation may be displayed. Note that any method of highlighting that the remaining days are based on a more accurate wear and tear estimation using data from sensing devices, such as using a specific color instead of a mark, is not limited.

[0066] Figure 10 shows an example of highlighting remaining days based on a more accurate wear estimation using data from a sensing device, but this is not the only example. For instance, the wear estimation method (wear estimation algorithm) and algorithm version could be displayed for all parts as information based on the type of wear calculation method. In other words, the type of wear calculation method (wear estimation algorithm) used to estimate the wear level used in life prediction could be displayed for each part. Furthermore, it is sufficient if the system user can recognize the accuracy of the remaining days according to the wear estimation method (wear estimation algorithm) used in the wear estimation, for example, by displaying a tooltip when the mouse hovers over the element.

[0067] In this embodiment, the method for estimating wear and tear was directly expressed in the form of an algorithm version, but this is not the only way. For example, the method for estimating wear and tear could be notified indirectly, such as by including both the wear and tear estimated based on condition data and the wear and tear estimated using the conventional counter method in the notification information.

[0068] As described above, in this embodiment, information on the wear and tear estimation method is acquired, and the accuracy of the remaining days according to the wear and tear estimation method information is displayed on the screen in a recognizable manner. This makes it possible for system users to recognize which component has high accuracy in its remaining lifespan, and which component's remaining lifespan was estimated using a highly accurate wear and tear estimation means (wear and tear estimation algorithm). In other words, system users can recognize the wear and tear calculation means used in a system that predicts the lifespan of consumable components of a device.

[0069] (Example 2) In Example 2, in addition to the processing in Example 1, a reset process for the lifespan prediction training data is performed due to a change in the wear and tear estimation means (wear and tear estimation algorithm). When the wear and tear estimation means (wear and tear estimation algorithm), indicated by the algorithm version, is changed due to a firmware update of device 102, etc., training data based on wear and tear obtained by two different algorithms becomes mixed. That is, the training data based on wear and tear obtained by the conventional algorithm and the training data based on wear and tear obtained by the new algorithm after the change become mixed. If the training data is mixed, machine learning will not be performed correctly, and it will negatively affect future predictions of remaining lifespan days. Therefore, in this example, the training data is cleared when the wear and tear estimation means (wear and tear estimation algorithm) is changed. Below, only the differences from Example 1 will be explained.

[0070] In step S604 of the component life prediction server 101's component life count update process (Figure 7), the component life prediction unit 303 calculates the remaining life count of the component to be processed. In this embodiment, when calculating the remaining life count, the component life prediction unit 303 further checks whether the algorithm version of the notification information 709 is the same as the algorithm version (life_version) stored as component information 800. If the algorithm versions are different, it can be determined that a change has occurred in the wear rate estimation algorithm due to a firmware update of device 102 or the like. If the algorithm versions are different, the new data will be mixed with the previous learning data based on the wear rate obtained with the conventional algorithm, which may negatively affect future remaining life count predictions. Therefore, the component life prediction unit 303 performs a process to clear the learning data for life count prediction.

[0071] As a process to clear the learning data for lifespan prediction, the component lifespan prediction unit 303 updates the component information to predetermined values ​​when updating the component information in step S605. Specifically, the component lifespan prediction unit 303 instructs the component information management unit 302 to update the daily lifespan progress (life_a_day) to "no value" and the flag indicating that initial learning is in progress (initial_learning) to "true". Furthermore, the component lifespan prediction unit 303 instructs the component information management unit 302 to update the learning start date (learning_start_date) to today's date. In this way, if the wear rate calculation means used to estimate the wear rate used in lifespan prediction is changed, the component lifespan prediction unit 303 resets the learning data for lifespan prediction that uses the wear rate estimated using the wear rate calculation means before the change.

[0072] When the training data is reset and the component information shows "No Value" for the daily lifespan progress and "True" for the flag indicating initial training, the application UI will display "Initial Training in Progress," and the remaining days for the component will not be displayed. The specific display on the application UI will be "*-- days (Initial Training in Progress)." Alternatively, after indicating that initial training is in progress, the conventional remaining days value for the component (the remaining days calculated based on the training data for conventional lifespan prediction) may be displayed as a reference value.

[0073] The period during which the remaining days for a component are hidden and the initial learning period are predetermined in the application specifications. For example, in this embodiment, the period during which the remaining days for a component are hidden is 6 days, and the initial learning period is 34 days. Therefore, after 7 days from the algorithm change, the remaining days for a component will be displayed in the application UI, and after 35 days, the "Initial Learning in Progress" message will no longer be displayed. In this way, when the learning data is reset, the UI management unit 305 displays a message in the UI for a predetermined period informing the system user that initial learning is in progress.

[0074] As explained above, in this embodiment, when a change in the component wear estimation algorithm is detected, the learning data for predicting remaining lifespan in days up to that point is reset. This prevents the remaining lifespan based on wear estimation by the previous algorithm from being mixed with the learning data, making it possible to predict the remaining lifespan in days more accurately.

[0075] (Example 3) In Example 2, a configuration was described in which the training data is reset by changing the wear rate calculation algorithm, and a message is displayed on the application UI indicating that initial training is underway for a predetermined period. In Example 3, when a change in the wear rate calculation algorithm is detected, the notification unit 306 is used to notify a pre-registered recipient, such as a system administrator, of the change in the wear rate calculation algorithm.

[0076] In S604, the component life prediction unit 303 checks whether the algorithm version of the notification information 709 is the same as the algorithm version (life_version) stored as component information 800. If the algorithm versions are different, it can be determined that a change has occurred in the wear rate estimation algorithm due to a firmware update of device 102 or the like. If the algorithm versions are different, the component life prediction unit 303 notifies the notification unit 306 that the wear rate estimation means (wear rate estimation algorithm) has been changed. Upon receiving the notification, the notification unit 306 notifies pre-registered recipients that the wear rate calculation algorithm has been changed via email, push notification to terminals, etc. The notification informing of the change in the wear rate calculation algorithm includes, for example, information that uniquely identifies the device and component type whose wear rate calculation algorithm has been changed, and details of the algorithm change. In this way, the notification unit 306 can notify system users that the wear rate calculation means used to estimate wear rate has been changed. Users who receive the notification can recognize that the wear rate calculation algorithm has been changed.

[0077] As explained above, this embodiment makes it possible for system users to be notified when there is a change in the device's wear and tear estimation algorithm. This allows system users to quickly take necessary actions to check the information affected by the change in the wear and tear estimation algorithm, such as reconfirming the remaining days in the system.

[0078] This embodiment includes the following system configuration. (Composition 1) A system for predicting the lifespan of consumable parts of a device, A receiving unit that receives data on consumable parts collected in the aforementioned device, including the degree of wear of the consumable parts and the wear calculation means used to estimate the degree of wear, A life prediction unit predicts the lifespan of a consumable part based on the degree of wear of the consumable part included in the data of the consumable part collected from the device, A system characterized by having a UI management unit that generates and provides a UI that displays information based on the type of wear degree calculation means, along with the predicted lifespan of the consumable parts. (Configuration 2) The system according to Configuration 1, characterized in that the UI management unit displays a notification indicating that the lifespan prediction is highly accurate when the wear rate calculation means used to estimate the wear rate for use in the lifespan prediction is a newer version than a predetermined version. (Composition 3) The system according to Configuration 1, characterized in that the UI management unit displays the type of wear and tear calculation means used to estimate the wear and tear used in predicting the lifespan. (Composition 4) The life prediction unit uses the learning data for life prediction to predict the life of the consumable parts based on the degree of wear, If the wear rate calculation means used to estimate the wear rate used in life prediction is changed, the life prediction unit resets the learning data for life prediction that uses the wear rate estimated using the wear rate calculation means before the change. The system according to any one of configurations 1 to 3, characterized in that, when the learning data is reset, the UI management unit displays a message indicating that initial learning is in progress on the UI for a predetermined period of time. (Composition 5) The system according to configuration 4, further comprising a notification unit that notifies the user of the system that the wear-out calculation means used for estimating the wear-out has been changed. (Composition 6) The aforementioned device is an image processing device equipped with a printing function, The wear degree calculation means is an algorithm for estimating the wear degree, The system according to any one of configurations 1 to 5, characterized in that the UI management unit provides the UI to the terminal of a user of the system via a web browser.

[0079] (Other embodiments) The present invention can also be realized by supplying a program that implements one or more of the functions of the above-described embodiments to a system or device via a network or storage medium, and by having one or more processors in the computer of that system or device read and execute the program. It can also be realized by a circuit (e.g., an ASIC) that implements one or more functions.

[0080] Although preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of its gist.

Claims

1. A system for predicting the lifespan of consumable parts of a device, A receiving unit that receives data on consumable parts collected in the aforementioned device, including the degree of wear of the consumable parts and the wear calculation means used to estimate the degree of wear, A life prediction unit predicts the lifespan of a consumable part based on the degree of wear of the consumable part included in the data of the consumable part collected from the device, A system characterized by having a UI management unit that generates and provides a UI that displays information based on the type of wear degree calculation means, along with the predicted lifespan of the wear parts.

2. The system according to claim 1, characterized in that the UI management unit displays a notification indicating that the lifespan prediction is highly accurate when the wear rate calculation means used to estimate the wear rate used in the lifespan prediction is a newer version than a predetermined version.

3. The system according to claim 1, characterized in that the UI management unit displays the type of wear calculation means used to estimate the wear level for use in predicting lifespan.

4. The life prediction unit uses the learning data for life prediction to predict the life of the consumable parts based on the degree of wear, If the wear rate calculation means used to estimate the wear rate used in life prediction is changed, the life prediction unit resets the learning data for life prediction that uses the wear rate estimated using the wear rate calculation means before the change. The system according to claim 1, characterized in that, when the learning data is reset, the UI management unit displays a notification that initial learning is in progress on the UI for a predetermined period of time.

5. The system according to claim 4, further comprising a notification unit that notifies the user of the system that the wear and tear calculation means used for estimating the wear and tear has been changed.

6. The aforementioned device is an image processing device equipped with a printing function, The wear degree calculation means is an algorithm for estimating the wear degree, The system according to claim 1, characterized in that the UI management unit provides the UI to the terminal of a user of the system via a web browser.

7. A control method for a system that predicts the lifespan of consumable parts of a device, The process of receiving data on consumable parts collected by the device, including the degree of wear of the consumable parts and the wear calculation means used to estimate the degree of wear, A step of predicting the lifespan of a consumable part based on the degree of wear of the consumable part included in the data of the consumable part collected from the device, A method for controlling a system, characterized by comprising the step of generating and providing a UI that displays information based on the type of wear degree calculation means, along with the predicted lifespan of the wear parts.

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