Abnormal sound diagnostic system, image forming apparatus, abnormal sound diagnostic method and program

Abstract

To provide a technique that can identify occurrence of abnormal noise and the cause of the abnormal noise, no matter whether the abnormal noise is known abnormal noise or unknown abnormal noise.SOLUTION: An abnormal noise diagnosis system, which identifies the cause of abnormal noise in a device equipped with a plurality of operating parts that perform predetermined operation and a plurality of driving parts that drive the plurality of operating parts, determines generation of abnormal noise in each of a plurality of time divisions, on the basis of sound wave levels measured in each of the plurality of time divisions, of sound occurring in the device, obtains a driving state of each of the plurality of driving parts, in each of the plurality of time divisions, obtains a plurality of comparison results corresponding to different timings by comparing a driving state with a generation state which are obtained at different timings of the predetermined operation, and identifies the driving part corresponding to generation of abnormal sound, out of the plurality of driving parts, on the basis of the plurality of comparison results.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to a abnormal sound diagnosis system, an image forming apparatus, an abnormal sound diagnosis method, and a program.

Background Art

[0002] Image forming apparatuses such as copiers and laser printers have replacement units that are replaced according to their lifespan. When a replacement unit is used beyond its lifespan, it may generate abnormal sounds depending on the state of the unit. For example, a conveyance roller disposed in a conveyance unit that conveys sheets may generate abnormal sounds due to wear between the roller shaft and the bearing. The occurrence of abnormal sounds is an indicator that the replacement unit has exceeded its lifespan or that a failure may occur, or it may cause discomfort to the user. Therefore, it is desirable to determine the occurrence of abnormal sounds and identify the replacement unit that is generating the abnormal sounds.

[0003] Patent Document 1 discloses a technique for detecting the occurrence of abnormal sounds and identifying the component that is generating the abnormal sounds by using a sound collector disposed inside an image forming apparatus to acquire operating sounds at a predetermined timing. The predetermined timing is the timing at which known abnormal sounds that are grasped by the developer for determining the state of the component occur.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, with the technique of Patent Document 1, only known abnormal sounds can be determined, and unknown abnormal sounds, that is, abnormal sounds that are not grasped by the developer during the development of the apparatus, cannot be determined.

[0006] This invention provides a technology that makes it possible to identify the occurrence and cause of an abnormal noise, regardless of whether it is a known or unknown abnormal noise. [Means for solving the problem]

[0007] An abnormal sound diagnostic system according to one aspect of the present invention comprises the following configuration: An abnormal noise diagnostic system for identifying the cause of abnormal noises occurring in an image forming apparatus comprising multiple operating parts and multiple motors that drive the multiple operating parts, The image forming period during which the image forming apparatus forms images on a predetermined number of recording materials based on the print data received by the image forming apparatus is included and During a plurality of periods in which at least one of the plurality of motors is in operation 、 A microphone configured to receive sound, A display means configured to display information, Get information means and The display means indicates the cause of the abnormal noise. 1 A notification means configured to notify the display of information, The plurality of motors includes a first motor and a second motor, The plurality of operating units include a first operating unit driven based on the driving force of the first motor and a second operating unit driven based on the driving force of the second motor, The aforementioned plurality of periods include a first period in which the first motor and the second motor are in operation, and a second period in which the second motor is in operation and the first motor is not in operation. before The occurrence of the aforementioned abnormal noise during the period specified in the first section is shown below. The information is the first abnormal sound information, and the The occurrence of the abnormal noise during the second period is shown. If the information is defined as second abnormal noise information, the acquisition means acquires second information based on the first abnormal noise information and the second abnormal noise information, indicating that the second operating unit is more likely than the first operating unit to be the cause of the abnormal noise. . [Effects of the Invention]

[0008] According to the present invention, it is possible to identify the occurrence and cause of an abnormal noise, regardless of whether it is a known or unknown abnormal noise. [Brief explanation of the drawing]

[0009] [Figure 1] A schematic diagram illustrating an example of the configuration of an image forming apparatus according to the first embodiment. [Figure 2] Block diagram showing an example of the hardware configuration of an image forming system according to the first embodiment. [Figure 3] Block diagram showing an example of the functional configuration of a control unit according to the first embodiment. [Figure 4] Flowchart showing an example of processing by a phonodiagnosis unit according to the first embodiment. [Figure 5] Graph showing an example of sound wave level data, statistical values, and threshold values according to the first embodiment in time series. [Figure 6] Explanatory diagram showing the result classified by a classification unit according to the first embodiment. [Figure 7] Flowchart showing an example of a abnormal sound determination unit according to the first embodiment. [Figure 8] Flowchart showing an example of a cause identification unit according to the first embodiment. [Figure 9] Explanatory diagram showing the determination result of a cause identification unit according to the first embodiment. [Figure 10] Explanatory diagram showing the determination result of a cause identification unit according to the second embodiment. [Figure 11] Flowchart showing an example of a cause identification unit according to the second embodiment. [Figure 12] Explanatory diagram showing the determination result of a cause identification unit according to the third embodiment. [Figure 13] Flowchart showing an example of a cause identification unit according to the third embodiment. [Figure 14] Explanatory diagram showing the determination result of a cause identification unit according to the fourth embodiment. [Figure 15] Flowchart showing an example of a cause identification unit according to the fourth embodiment.

Mode for Carrying Out the Invention

[0010] The embodiments will be described in detail below with reference to the attached drawings. Note that the following embodiments do not limit the invention as defined in the claims. While the embodiments describe multiple features, not all of these features are essential to the invention, and the features may be combined in any way. Furthermore, in the attached drawings, identical or similar configurations are given the same reference numerals, and redundant descriptions are omitted.

[0011] <First Embodiment> [Description of the image forming apparatus] An electrophotographic image forming apparatus to which embodiments of the present invention can be applied will be outlined. Figure 1 is a diagram showing a schematic configuration example of a printer 100 that employs an intermediate transfer belt and has multiple image forming units configured in parallel.

[0012] Printer 100 is a tandem-type color laser beam printer configured to output color images by layering four toners: yellow (Y), magenta (M), cyan (C), and black (K). In the following description, for the sake of clarity, the subscripts Y, M, C, and K will be omitted for components where there is no need to distinguish between yellow, magenta, cyan, and black.

[0013] The process cartridge 5 includes a toner container 6, a photoreceptor drum 1 which is an image carrier, a charging roller 2, a developing roller 3, a drum cleaning blade 4, and a drum waste toner container 7. The laser unit 8 is positioned below the process cartridge 5 and performs exposure on the photoreceptor drum 1 based on an image signal. The photoreceptor drum 1 is charged to a predetermined negative potential by applying a predetermined negative voltage to the charging roller 2, and then electrostatic latent images are formed on it by the laser unit 8. These electrostatic latent images are reverse-developed by applying a predetermined negative voltage to the developing roller 3, and toner images of Y, M, C, and K are formed on the photoreceptor drum 1. The toner used in this embodiment is negatively charged.

[0014] The intermediate transfer unit comprises an intermediate transfer body 11, a tension roller 13, a drive roller 15, an intermediate transfer body cleaning blade 16, and a waste toner collection container 17. A primary transfer roller 10 is positioned inside the intermediate transfer body 11, facing the photoreceptor drum 1, and a transfer voltage is applied by a voltage application means (not shown). The toner image formed on the photoreceptor drum 1 is primarily transferred onto the intermediate transfer body 11 as each photoreceptor drum 1 and intermediate transfer body 11 rotates in the direction of the arrow, and a positive voltage is applied to the primary transfer roller 10. The toner image on the photoreceptor drum 1 is primarily transferred onto the intermediate transfer body 11 in the order of Y, M, C, and K, and the four overlapping toner images are transported to the secondary transfer roller 14. Untransferable toner remains on the photoreceptor drum 1 and intermediate transfer body 11. This is cleaned by the drum cleaning blade 4 and the intermediate transfer body cleaning blade 16, and collected in the drum waste toner container 7 and the waste toner collection container 17, respectively.

[0015] The paper feeding mechanism 20 includes a paper feeding roller 22 for feeding recording material S contained in a paper feeding cassette 21, a transport roller 23 for transporting the fed recording material S, a separation roller 24 for separating and transporting the recording material S one sheet at a time, and a pair of registration rollers 25. The recording material S transported from the paper feeding mechanism 20 is transported to the secondary transfer roller 14 by the pair of registration rollers 25. At this time, a transport sensor 90 detects that the recording material S is being transported downstream of the pair of registration rollers 25. A positive voltage is applied to the secondary transfer roller 14 in order to transfer the toner image from the intermediate transfer body 11 to the recording material S. This secondarily transfers the toner image on the intermediate transfer body 11 to the transported recording material S. The recording material S with the transferred toner image is transported to the fixing unit 30, where it is heated and pressurized by the fixing film 31 and pressure roller 32 to fix the toner image on its surface. The fixed recording material S is discharged by the paper discharge roller pair 33.

[0016] The printer 100 has a receiving unit 71 for receiving sound waves, which is positioned between the transport sensor 90 and the secondary transfer roller 14. The receiving unit 71 has a MEMS (Micro Electro Mechanical System) microphone that converts the vibrational displacement of the diaphragm due to pressure into a voltage change and outputs it. Note that any microphone other than a MEMS microphone, such as a condenser microphone, can be used if it is capable of receiving sound waves. The temperature detection unit 72 detects the temperature inside the printer 100. The temperature detection unit 72 is a configuration used in the third embodiment and may be omitted in embodiments other than the third embodiment.

[0017] [Description of hardware configuration] Figure 2 is a block diagram showing an example of the hardware configuration of the image forming system in this embodiment. As shown in Figure 2, the hardware configuration of this embodiment consists of a printer 100, a host computer 200, and a server 300. The host computer 200 has a main unit 201 that instructs the printer 100 to print via the network and an operation display unit 202. Here, the operation display unit 202 of the host computer 200 includes a display, keyboard, mouse, etc. (not shown). The printer 100 as an image forming apparatus and the server 300 as an information processing apparatus constitute an abnormal noise diagnostic system that identifies the occurrence of abnormal noises in the image forming apparatus and their causes.

[0018] The printer 100 includes a video controller 101, an operation display unit 102, and a printer engine 103. The operation display unit 102 of the printer 100 includes an operation panel and operation buttons (not shown). The video controller 101 transmits print data, print instructions, and print setting information such as the type of recording material sent from the host computer 200 to the printer engine 103.

[0019] The printer engine 103 consists of an engine control unit 110 including a CPU 180, ROM 181, and RAM 182, a system bus 104, and an I / O port 105. The CPU 180 executes the program stored in ROM 181, using RAM 182 as a workspace. The aforementioned components can access the I / O port 105 via the bidirectional system bus 104. The transport sensor 90 and various motors are connected to the I / O port 105.

[0020] In the example of the printer 100 according to this embodiment, the motors are of the following four types: The paper feed motor 91 drives the paper feed roller 22, transport roller 23, and registration roller pair 25 that transport the sheet S. The intermediate transfer body / K photoreceptor drum motor 92 drives the drive roller 15, thereby rotating the intermediate transfer body 11 in the direction of the arrow in Figure 1. At the same time, it rotates the K photoreceptor drum 1K in the direction of the arrow in Figure 1. The YMC photoreceptor drum motor 93 drives the YMC photoreceptor drums 1Y, 1M, and 1C in the direction of the arrow in Figure 1. The intermediate transfer body / K photoreceptor drum motor 92 and the YMC photoreceptor drum motor 93 are located in a drive unit (not shown). The fuser motor 95 drives the pressure roller 32 of the fuser unit 30.

[0021] The engine control unit 110 (CPU 180) controls these actuators via the IO port 105 based on print setting information and the like transmitted from the host computer 200. For example, if the print setting information indicates that the type of recording material is plain paper (recording material with a basis weight of around 80 g / m2), the actuator speed is controlled to match the plain paper setting in the printer for printing. If the material is thick paper (with a basis weight of around 120 g / m2), the actuator speed is set to half the speed for plain paper to improve the fixing performance of the fuser unit.

[0022] Server 300 has a server control unit 301 which includes a computing unit 311 and a storage device 312, and is connected to printer 100 by a bidirectional network. The computing unit 311 executes programs stored in the storage device 312 and reads and writes various data. The computing unit 311 may be directly allocated a CPU or GPU, and the storage device 312 may be directly allocated RAM, HDD or SSD, or a virtual environment such as a virtual machine may be allocated. The server control unit 301 can exchange information with the engine control unit 110 via the video controller 101.

[0023] [Explanation of Functional Block Diagram] The functions of the engine control unit 110 and the server control unit 301 will now be described. Figure 3 is a block diagram showing an example of the functional configuration of the engine control unit 110 and the server control unit 301. The functions of the engine control unit 110 can be realized, for example, by the CPU 180 executing a predetermined program stored in the ROM 181. The functions of the server control unit 301 can be realized by the arithmetic unit 311 executing a predetermined program stored in the storage device 312. The functions of the engine control unit 110 and the server control unit 301 may be realized by dedicated hardware, or by the cooperation of software and hardware. The engine control unit 110 has the function of processing received sound and the function of obtaining sound data by adding sensor and motor information to the sound information obtained by processing the received sound. The server control unit 301 has the function of identifying whether an abnormal sound has occurred from the sound data and the function of identifying the component that is generating the abnormal sound. Each will be described in turn.

[0024] The engine control unit 110 includes a received sound processing unit 140, a sound wave information processing unit 150, and a status notification unit 160. When the printer engine 103 receives a print command, the engine control unit 110 measures the received sound using the receiving unit 71 at a predetermined timing, which will be described later. Subsequently, each functional unit of the received sound processing unit 140 processes the received sound received by the receiving unit 71 as follows: The received sound amplification unit 141 amplifies the voltage indicating the level of the received sound (internal operating sound of the printer 100) received by the receiving unit 71. The AD conversion unit 142 converts the voltage output by the received sound amplification unit 141 into a digital signal. Since the voltage output by the receiving unit 71 is a positive value, it is necessary to remove the DC component and extract the sound pressure fluctuations. The reference value setting unit 143 subtracts the reference value from each value indicated by the digital signal input from the AD conversion unit 142 to extract the sound pressure fluctuations.

[0025] The squaring unit 144 performs squaring of the digital signal after the reference value has been set by the reference value setting unit 143. The interval averaging unit 145 performs interval averaging on the digital signal after the squaring calculation has been performed by the squaring unit 144. For example, the time interval for performing interval averaging is 100 ms. The time length for performing interval averaging is not limited to this and can be varied for each measurement. Through squaring and interval averaging, the digital signal after the reference value has been set becomes time-series sound wave level data indicating the magnitude of sound pressure fluctuations for each time interval. The sound wave level data is stored in the sound wave information processing unit 150. The status notification unit 160 notifies the sound wave information processing unit 150 of information from the sensor and information representing the drive status of motors, etc. (hereinafter referred to as actuator information). The sound wave information processing unit 150 synchronizes the sound wave level data from the received sound processing unit 140 with the drive status of the actuator indicated by the actuator information notified from the status notification unit 160. Details will be described later. The sound data processed by the sound wave information processing unit 150 (data associating the state of the actuator with the sound wave level data) is also stored in the storage device 312 of the server control unit 301 via the video controller 101. The drive control unit 170 controls the driving of multiple actuators (for example, the four types of motors described above). The state notification unit 160 may acquire actuator information from each actuator, or it may acquire actuator information from the drive instruction signals to the actuators output by the drive control unit 170.

[0026] The sound diagnostic unit 320 analyzes sound data to determine whether or not an abnormal noise is occurring and identifies the cause of the abnormal noise. The sound diagnostic unit 320 includes a classification unit 321, a statistical calculation unit 322, a threshold setting unit 323, an abnormal noise determination unit 324, and a cause identification unit 325. The classification unit 321 classifies the set of sound data stored in the storage device 312 into multiple subsets based on predetermined criteria. The statistical calculation unit 322 calculates statistical values ​​from each classified subset. The threshold setting unit 323 further statistically processes the statistical values ​​calculated by the statistical calculation unit 322 to set thresholds. The abnormal noise determination unit 324 determines whether or not an abnormal noise is occurring in the subset. The cause identification unit 325 identifies the unit or component emitting the abnormal noise from the multiple abnormal noise determination results from the abnormal noise determination unit 324 (cause identification). The result of cause identification is notified by the notification unit 330 to a host computer 200 such as a user or dealer, a printer management tool (not shown), etc.

[0027] Next, we will explain the timing of sound measurement in the receiving unit 71 and the sound information synchronized in the sound wave information processing unit 150.

[0028] The sound receiving unit 140 measures the sound inside the printer 100 at different timings during the image formation operation on the recording material. In this embodiment, the sound receiving unit 140 measures the sound inside the printer 100 using the receiving unit 71 at the following two timings (first measurement and second measurement). In the first measurement, the sound receiving is measured from the time a print command is issued and the recording material S is fed by the paper feed roller 22 until 1600 ms have elapsed when the recording material S reaches the secondary transfer roller 14. In the second measurement, the sound receiving is measured from the time the trailing end of the final recording material passes the transport sensor 90 until 1600 ms have elapsed when the printer stops operating. The measurement time and timing are not limited to this example. The measurement time can be set arbitrarily, but by limiting the measurement time, the load on the printer 100 and the server control unit 301 and the constraints on memory capacity due to the increase in measurement data can be reduced. As mentioned above, in this embodiment, the interval at which the interval averaging calculation unit 145 performs interval averaging calculations is set to 100 ms, so for both the first and second measurements, data for 16 intervals is collected with a measurement time of 1600 ms.

[0029] In the first and second measurements, the receiving sound processing unit 140 acquires and stores sound wave level data for each section by performing the aforementioned interval averaging calculation when the measurement starts. As a result, sound wave level data for 16 time sections, from data 1 to data 16, is acquired and stored. The sound wave information processing unit 150 also acquires actuator information notified by the status notification unit 160 for the same time section (100 ms) as the interval averaging calculation in which the sound wave level data is calculated, and associates the sound wave level data with the actuator information. In this way, the sound wave information processing unit 150 acquires data (hereinafter referred to as sound data) in which the sound wave level data and actuator information are synchronized, and provides it to the server control unit 301 (sound diagnosis unit 320). If there is no need to reduce the load on the printer 100 or the server control unit 301, the measurement may be continued continuously, and the analysis section may be specified in the processing of the sound diagnosis unit 320 described later. The time interval between sound wave level data and actuator information is set to 100 ms, but it is not limited to this. For example, the sound wave level data and actuator information may be synchronized over a finer time section.

[0030] [Operation description of the sound diagnostic unit 320] Figure 4 is a flowchart showing an example of the statistical generation process performed by the server 300 in this embodiment, specifically the process related to the classification of sound data. In the following description, processing steps will be abbreviated as S (step).

[0031] In S101, the classification unit 321 checks if there is any newly input sound data in the storage device 312 of the server control unit 301. If there is new input data, the classification unit 321 starts classifying the new sound data. In S102, the classification unit 321 classifies the sound data into different groups according to the measurement timing. In this embodiment, it is classified into two types: the first measurement and the second measurement described above. Next, in S103, the classification unit 321 classifies the sound data into different groups according to print setting information such as the type of recording material. For example, since the operating speed of the actuator changes depending on the type of recording material such as plain paper or cardboard, the sound data is classified into different groups depending on the type of recording material. In S104, the classification unit 321 classifies the sound data into groups in which the drive and stop states of the actuators for all 16 sections of the sound data from data 1 to data 16 are the same. In this embodiment, the classification is performed so that the operating timings of all actuators notified by the status notification unit 160 match, but it is not limited to this, and classification may be performed if the operating timings of some actuators are the same. In other words, the timing of the operation of all actuators does not need to be the same for sound data belonging to the same group. Sound data may be classified into groups by focusing on the timing of the operation of at least one actuator. Note that the classification method is not limited to the above. For example, in addition to the above classification, a step may be added to classify the data based on whether the image forming operation is monochrome or multi-color.

[0032] The statistics calculation unit 322 calculates statistical values ​​P for each of the 16 time intervals for each subset of sound wave level data classified by the classification unit 321. In S105, the statistics calculation unit 322 calculates statistical values ​​P for a predetermined number of the most recent data points of the sound wave level data for each subset (each classification). In this embodiment, the statistical value P is set to the data point in the top 5% of the most recent 100 data points (the 5th data point from the top of the sound wave level data).

[0033] In S106, the threshold setting unit 323 determines whether 100 or more statistical values ​​P have been calculated. This means that 100 × 100 = 10,000 sound wave level data points have been acquired at this stage. If it is determined that there are 100 or more statistical values ​​P (YES in S106), the process proceeds to S107, where the threshold setting unit 323 determines whether a threshold TH-P has been set. If it is determined that a threshold TH-P has not been set (NO in S107), the process proceeds to S108. In S108, the threshold setting unit 323 performs statistical processing on the 100 statistical values ​​P to set the threshold TH-P. For example, the threshold setting unit 323 calculates the average value of the 100 statistical values ​​P. Figure 5 is a graph showing the number of prints on the horizontal axis and sound wave level data on the vertical axis. The threshold TH-P is set to a value of 10 dB from the average value of the statistical values ​​P shown by the solid line. If the threshold TH-P has already been set in S107 (YES in S107), or if it is determined in S106 that the statistical value P is less than 100 data points (NO in S106), the threshold setting unit 323 does not set a threshold and terminates this process. Note that the method for calculating the statistical value P is not limited to the method described above. For example, the statistical value P can be the median or maximum value of any most recent sound wave level data. Similarly, the method for setting the threshold TH-P is not limited to the method described above. For example, the threshold TH-P can be the median or maximum value of any number of statistical values ​​P increased by a predetermined method.

[0034] Figure 6 shows an example of acquiring sound wave level data and actuator information. It shows how sound wave level data and information indicating whether or not the actuator is driven have been acquired for multiple time intervals (in this example, 16 time intervals, data 1 to 16). Figure 6 also shows an example of the results of classifying the sound data obtained in the second measurement using the operating state of the actuator in S104, by the classification unit 321. The item group 610 from the paper feed motor to the fuser motor is actuator information, and it indicates whether or not each motor is driven in each time interval. In the actuator information, '1' represents the driven state and '0' represents the stopped state. As shown in Figure 6, in this embodiment, the second measurement is classified into two groups, group A and group B, based on the actuator information. In group A and group B, the stopping timing of the YMC photoreceptor drum motor, as indicated by the hatching in the table, is different. By classifying according to the operating state of the actuator in this way, the variation in sound wave level data is reduced, and a stable subset of sound wave levels can be formed.

[0035] Figure 7 is a flowchart showing an example of the processing of the abnormal sound detection unit 324 in this embodiment. The processing in S201 to S207 is the abnormal sound detection process performed for each of the 16 time intervals for each classification performed in S102 to S104. In S202, the abnormal sound detection unit 324 determines whether or not the threshold TH-P has been set by S108. If the threshold TH-P has not yet been set (NO in S202), the process proceeds to S208, and the abnormal sound detection unit 324 determines that the abnormal sound level for that classification is "unknown". If the threshold TH-P has been set (YES in S202), the process proceeds to S203. In S203, the abnormal sound detection unit 324 determines whether or not a new statistical value P has been calculated by S105. If it is determined that a new statistical value P has not been calculated for the classification to be processed (NO in S203), the abnormal sound detection unit 324 moves the processing to the next classification. If it is determined that a new statistical value P has been calculated (YES in S203), the process proceeds to S204. In S204, the abnormal noise detection unit 324 determines whether the new statistical value P is greater than or equal to the threshold TH-P. If it is determined to be greater than or equal to the threshold (YES in S204), the process proceeds to S205, where the abnormal noise detection unit 324 determines that an abnormal noise has occurred and sets the abnormal noise level to '1' for that classification. If it is determined that the new statistical value P is less than the threshold (NO in S204), the abnormal noise detection unit 324 determines that it is normal and sets the abnormal noise level for that classification to '0' in S206. The above process is repeated for all classifications until the above determination is completed (S207).

[0036] The cause identification unit 325 compares the noise generation state and the actuator's drive state at each measurement timing to obtain multiple comparison results and identifies the cause of the noise based on these multiple comparison results. A specific example of the process of identifying the cause of the noise from a comparison of the generation state and the drive state will be described below. Figure 8 is a flowchart of an example of the processing of the cause identification unit 325 in this embodiment. The processing in S301 to S303 is performed for each measurement timing and for each actuator with respect to sound data whose noise level has been determined by the noise determination unit 324 (S205, S206). In S302, the cause identification unit 325 compares the noise generation state and the actuator's drive state and determines similarity based on the comparison result. An example of a specific method for determining similarity will be described below. The cause identification unit 325 considers the sequence of representations of the presence or absence of noise in data 1 to data 16 (generation state) and the sequence of representations of the presence or absence of a specific actuator's drive (drive state) as 16-dimensional vectors, and calculates the cosine (hereinafter, COS) similarity between the two. When the abnormal noise level is vector A and the operating state of the actuator is vector B, the COS similarity is expressed by the following formula [Equation 1].

[0037]

number

[0038] In this embodiment, the closer the COS similarity is to 1, that is, the higher the COS similarity, the more likely it is that the actuator is emitting an abnormal noise. Note that if no abnormal noise is occurring and the abnormal noise level in data 1 to 16 is 0, the abnormal noise level vector A will also be zero, and therefore the COS similarity cannot be calculated. Accordingly, in this embodiment, the COS similarity is set to zero when the abnormal noise level vector A is zero. While this embodiment uses COS similarity as a method for determining similarity, other methods may also be used.

[0039] In the following explanation, we will use one classification each for the first and second measurements from among several classifications. Figures 9(a) and (b) show the results of calculating the COS similarity of the actuator at the first and second measurement timings, and indicate the abnormal noise level and the operating state of the actuator in 16 time intervals from data 1 to 16. In the first measurement, the abnormal noise level is '1' for all data from 1 to 16. On the other hand, the operating state of the YMC photoreceptor drum motor shows '0', indicating a stopped state, from data 1 to 13, and '1', indicating an operating state, from data 14 to 16. The COS similarity calculated by treating these two data as vectors is 0.43. The cause identification unit 325 repeats the above process until it has finished executing for all measurement timings and actuators.

[0040] Returning to Figure 8, in steps S304 to S306, the cause identification unit 325 calculates the average of the COS similarity calculated in steps S301 to S303 for each actuator. Steps S304 and S306 indicate that the process is repeated to process each of the actuators. In the example in Figures 9(a) and 9(b), the cause identification unit 325 identifies the cause based on the results of calculating the COS similarity for two measurement timings. In step S305, the cause identification unit 325 calculates the average value of the COS similarity calculated for all measurement timings for each actuator. The cause identification unit 325 repeats this averaging of COS similarity for all actuators until it has finished (S306). Figure 9(c) shows the result of the cause identification unit 325 averaging all actuators. In step S307, the cause identification unit 325 identifies the actuator corresponding to the occurrence of the internal noise among the multiple actuators based on the averaged COS similarity. In this embodiment, since the COS similarity of the fuser motor 95 is 1.00, the cause identification unit 325 determines that it is the actuator causing the abnormal noise. The cause identification unit 325 also identifies the actuator that is driven at least partially by the actuator determined to be the cause of the abnormal noise from among the multiple operating parts as the cause of the abnormal noise. In this example, since the fuser motor 95 drives the fuser unit 30, the cause identification unit 325 identifies the fuser unit 30 as the unit causing the abnormal noise. In step S308, the notification unit 330 notifies the engine control unit 110, a host computer 200 such as a user or dealer, a printer management tool (not shown), etc., of the result of the cause identification by communication. The notification unit 330 may also notify a display device provided by the server 300. Alternatively, the notification unit 330 may notify the printer 100 of the result of the cause identification and display it on the display unit of the printer 100.

[0041] It should be noted that depending on the measurement timing, there may be cases where there is insufficient data and the abnormal noise level cannot be determined. In such cases, a value of '-1' is set to indicate unknown COS similarity, and the average COS similarity is lowered. This prevents the cause from being identified when data from all measurement timings is not available.

[0042] As described above, according to the first embodiment, the actuator or unit emitting the abnormal noise can be identified whether the abnormal noise is known or unknown. In the above, the cause of the abnormal noise was identified based on the state of abnormal noise generation and the operating state of the actuator at two measurement timings (based on the average value of COS similarity), but this is not the only way. For example, the cause of the abnormal noise may be identified based on the state of abnormal noise generation and the operating state of the actuator at one measurement timing. However, by using multiple measurement timings, the accuracy of identifying the cause of the abnormal noise can be improved. For example, in this embodiment, with only the first measurement, the cause of the abnormal noise could only be narrowed down to two actuators with high COS similarity: the paper feed motor and the fuser motor. With the first embodiment, however, the cause can be narrowed down to just the fuser motor.

[0043] Furthermore, when an unusual noise occurs, users and dealers will be notified of the cause, allowing them to address the issue quickly. In particular, dealers can resolve the printer 100 malfunction in a single user visit, preventing unnecessary visits.

[0044] Furthermore, the actuators can include not only motors but also sensors, solenoids, electromagnetic clutches, etc., and it is possible to identify the cause in more detail from their operating state and abnormal noise level. Also, in the above, the cause identification unit 325 identified the actuator corresponding to the abnormal noise when the COS similarity was 1.00, but it is not limited to this. An actuator that obtains a COS similarity greater than a predetermined threshold may be identified as the actuator corresponding to the abnormal noise. In addition, in this embodiment, only one threshold TH-P was set, but it is not limited to this. For example, two thresholds TH-P may be set, and the abnormal noise level may be set to three levels: '0', '1', and '2'. In this case, when calculating the COS similarity, an abnormal noise level vector A is generated by setting abnormal noise levels '1' and '2' to 1. This is because if the components of vector A include both '1' and '2', the COS similarity will be lower than that of vector A composed only of '1'. It is undesirable for the COS similarity to change even though the abnormal noise occurs at the same time. Therefore, when calculating the COS similarity, as described above, all vector components of '1' and '2' are replaced with '1'. After the source of the abnormal noise is identified by the cause identification unit 325, if the sound data contains abnormal noise level '2', the notification unit 330 notifies the dealer that a severe abnormal noise has occurred and prompts them to take immediate action. On the other hand, if the sound data contains only abnormal noise level '1', the notification unit 330 may notify the dealer that a minor abnormal noise has occurred and prompt them to prepare for action. Alternatively, the abnormal noise level vector A may be generated with abnormal noise levels '1' and '2' as they are, and the COS similarity may be calculated to identify the cause.

[0045] <Second Embodiment> In the first embodiment, sound was measured during normal printing (image forming operation) to identify the cause of abnormal noise. In the second embodiment, sound is measured during a calibration operation, which corrects the toner density to ensure proper image formation, and the cause of abnormal noise is identified based on the results. The second embodiment will be described below, focusing on the differences from the first embodiment.

[0046] Calibration consists of two types of operations: monochrome operation (hereinafter referred to as "monochrome operation"), which corrects the conditions for a single color, and full-color operation (hereinafter referred to as "full-color operation"), which corrects the conditions for multiple colors. These operations are performed according to the user's printing habits. For example, users who frequently print in full color will have a higher proportion of full-color calibrations. In addition, different actuators are driven in monochrome operation and full-color operation. In monochrome operation, the intermediate transfer body / K photoconductor drum motor 92 is driven, while in full-color operation, the YMC photoconductor drum motor 93 is added to the motor used in monochrome operation.

[0047] During calibration, sound measurements are taken at the moment when the actuators, whether in monochrome or full-color operation, are constantly running. Therefore, if an abnormal noise is detected, it is presumed that all the motors being driven are the source of the noise.

[0048] Figure 10 is a table showing the results of measuring the sound during calibration and calculating the COS similarity using the cause identification unit 325 described in the first embodiment. As shown in the table, in full-color operation, the COS similarity of the intermediate transfer body / K photoreceptor drum motor 92 and the YMC photoreceptor drum motor 93 is high. This indicates that there is a possibility that abnormal noise is occurring in either the intermediate transfer body unit, the YMCK process cartridges, or the drive unit where their motors are located. On the other hand, in monochrome operation, the COS similarity of the intermediate transfer body / K photoreceptor drum motor 92 is 0.0, meaning that no abnormal noise is occurring. Based on the above COS similarity calculation results, the cause of the abnormal noise will be identified.

[0049] Figure 11 is a flowchart showing the process by which the cause identification unit 325 identifies the cause of abnormal noise during calibration. In S401, the cause identification unit 325 starts processing each of several possible causes of abnormal noise in order to determine whether it is the cause of the abnormal noise. The multiple candidates are, for example, the intermediate transfer unit or K process cartridge, each YMC process cartridge, and the drive unit (intermediate transfer unit / K photoreceptor drum motor 92 or YMC photoreceptor drum motor 93).

[0050] To determine whether the drive unit is the cause of the abnormal noise, in S402, the cause identification unit 325 determines whether the abnormal noise occurs during monochrome operation (displayed as "Mono" in the flowchart) or full-color operation (displayed as "Full" in the flowchart). This is because even if the abnormal noise occurs during either monochrome or full-color operation, the drive unit driving them may be the source of the noise. If it is determined in S402 that an abnormal noise is occurring (YES in S402), the process proceeds to S403, where the cause identification unit 325 determines that the drive unit may be the cause of the abnormal noise. If it is determined that no abnormal noise is occurring (NO in S402), in S404, the cause identification unit 325 determines that the drive unit is functioning normally. In the case of the COS similarity result in Figure 10, since the abnormal noise occurs during full-color operation, the cause identification unit 325 determines that the drive unit may be the cause of the abnormal noise.

[0051] Next, the process for determining whether the intermediate transfer unit or the K process cartridge is the cause of the abnormal noise will be described. If the intermediate transfer unit or the K process cartridge is the cause, the abnormal noise should occur in both monochrome and full-color operation. First, in S405, the cause identification unit 325 determines whether there is data (calculated COS similarity) for both monochrome and full-color operation. If it is determined that there is data (COS similarity) for both (YES in S405), in S406, the cause identification unit 325 determines whether the abnormal noise occurs in both monochrome and full-color operation. If it is determined that the abnormal noise occurs in both (YES in S406), the process proceeds to S407, where the cause identification unit 325 determines that the intermediate transfer unit or the K process cartridge is likely the cause of the abnormal noise. If it is determined that the abnormal noise does not occur in at least one of the monochrome or full-color operation (NO in S406), that is, if the abnormal noise occurred in only one or neither, the process proceeds to S408. In S408, the cause identification unit 325 determines that the intermediate transfer unit or K process cartridge is normal. For example, in the case of the COS similarity shown in Figure 10, since no abnormal noise is generated during monochrome operation, the intermediate transfer unit or K process cartridge is determined to be normal.

[0052] If it is determined in S405 that data for both monochrome and full-color operation is not available (NO in S405), the process proceeds to S409. In S409, the cause identification unit 325 determines whether or not an abnormal noise is occurring in the operation mode in which data exists. If it is determined that an abnormal noise is occurring (YES in S409), the process proceeds to S407, where the cause identification unit 325 determines that the intermediate transfer unit or the K process cartridge may be the cause of the abnormal noise. If it is determined that no abnormal noise is occurring (NO in S409), the process proceeds to S408, where the cause identification unit 325 determines that the intermediate transfer unit and the K process cartridge are functioning normally.

[0053] Next, the process for determining whether each YMC process cartridge is the cause of the abnormal noise will be explained. If each YMC process cartridge is the cause of the abnormal noise, the abnormal noise should only occur during full-color operation. Therefore, in S410, the cause identification unit 325 determines whether or not an abnormal noise occurs during full-color operation. If it is determined that an abnormal noise occurs during full-color operation (YES in S410), in S411, the cause identification unit 325 determines that one of the YMC process cartridges may be the cause of the abnormal noise. If it is determined that no abnormal noise occurs during full-color operation (NO in S410), in S412, the cause identification unit 325 determines that each YMC process cartridge is normal.

[0054] If the cause identification unit 325 identifies the cause through the above process and obtains a COS similarity score as shown in Figure 10, the cause of the abnormal noise can be narrowed down to each process cartridge or drive unit of the YMC.

[0055] As described above, according to the second embodiment, the cause of abnormal noise can be narrowed down more effectively than identifying the cause of abnormal noise during monochrome operation or full-color operation individually. In this embodiment, calibration was used as an example, but it is not limited to this, and for example, the operating sounds immediately after turning on the power or during cleaning sequences that clean the photosensitive drum or intermediate transfer material may be measured and applied to identify the cause.

[0056] <Third Embodiment> The image forming apparatus of the third embodiment is equipped with two types of cleaning blades: a drum cleaning blade 4 and an intermediate transfer body cleaning blade 16. These cleaning blades scrape off toner by contacting the drum and intermediate transfer body, generating friction at the contact point. This friction causes the tip of the cleaning blade to vibrate, which can generate abnormal noise. In particular, at low temperatures (below 10°C), the rubber of the cleaning blade hardens, making abnormal noise more likely. On the other hand, during continuous printing, when the image forming apparatus is constantly operating, the cleaning blade heats up due to friction, making it less likely for abnormal noise to occur. In the third embodiment, this principle is applied to improve the accuracy of identifying the cause of abnormal noise. The third embodiment will be described below, focusing on the differences from the first and second embodiments.

[0057] The schematic configuration of the printer 100 according to the third embodiment is shown in Figure 1. The printer 100 of this embodiment has a temperature detection unit 72 inside for detecting temperature. In this embodiment, the threshold is set to 10°C, and if the temperature detected by the temperature detection unit 72 is below the threshold, it is determined to be a low-temperature state, and if it is above the threshold, it is determined to be a high-temperature state, but of course, it is not limited to this.

[0058] Figure 12 is a table showing the results of measuring the sound during calibration when a low temperature state is determined and when a high temperature state is determined based on the detection results by the temperature detection unit 72, and calculating the COS similarity by the cause identification unit 325. In this embodiment, as shown in the table, the COS similarity is high only in full-color operation in the low temperature state.

[0059] Figure 13 is a flowchart showing the cause identification process according to the third embodiment. Note that in Figure 13, some parts of the flowchart are the same as in the second embodiment (Figure 11) and are therefore omitted.

[0060] If the abnormal noise occurs only at low temperatures, then, as described above, it can be determined that the cleaning blade is generating the noise and the drive unit is not. Therefore, in S501, the cause identification unit 325 first determines whether the abnormal noise occurs only at low temperatures. If the abnormal noise occurs only at low temperatures (YES in S501), the process proceeds to S502, and the cause identification unit 325 determines that the drive unit is functioning correctly. On the other hand, if the abnormal noise occurs at temperatures other than low temperatures (NO in S501), the process proceeds to S503, and the cause identification unit 325 determines whether the abnormal noise occurs during monochrome operation or full-color operation. If it is determined that no abnormal noise occurs (NO in S503), the process proceeds to S502, and the cause identification unit 325 determines that the drive unit is functioning correctly. If it is determined that an abnormal noise is occurring (YES in S503), the process proceeds to S504, and the cause identification unit 325 determines that the drive unit may be the cause of the abnormal noise. The determination process for other units is the same as in the second embodiment. That is, S505 corresponds to S405 to S409, and S506 corresponds to S410 to S412. In the example shown in Figure 12, the COS similarity is high (abnormal noise occurs) only during full-color operation at low temperatures, so the drive unit is normal (S502), and the cause of the abnormal noise can be identified as one of the YMC process cartridges.

[0061] As described above, according to the third embodiment, by applying the occurrence of abnormal noise in two types of conditions, low temperature and high temperature, to cause identification, it is possible to determine, for example, that the drive unit is the cause of the abnormal noise, thereby improving the accuracy of cause identification. In this embodiment, the cause was identified using the two types of operation, monochrome operation and full-color operation, and the internal temperature, as described in the second embodiment, but the accuracy of cause identification can be improved even if, for example, only data for monochrome operation is available. In addition, in this embodiment, the temperature detection unit 72 is placed inside the device, but the accuracy of cause identification can be improved. For example, the temperature detection unit 72 may not be placed, and a prediction algorithm that predicts the internal temperature from the number and frequency of continuous prints may be applied.

[0062] <Fourth Embodiment> In the first embodiment, the cause of the abnormal noise was identified by measuring sound in conjunction with the image forming operation. In the fourth embodiment, a configuration is described in which the cause of the abnormal noise can be identified with greater accuracy by using only the second measurement, which is taken when the trailing end of the final recording material has passed through the receiving unit 71 and the noise level is relatively low. The fourth embodiment will be described below, focusing on the differences from the first embodiment.

[0063] For example, when identifying the cause in the second measurement, as shown in Figure 14(a), if the abnormal noise level and the timing of the operation of the paper feed motor 91 match, this data alone can be used to identify the paper feed motor 91 as the cause of the abnormal noise. However, because the sound waves and motor drive timing are calculated using interval averaging, as shown in Figure 14(b), the abnormal noise level and the state of the actuator (motor operation timing) may not perfectly match (the measurement intervals are recorded with a discrepancy). In such cases, even the paper feed motor 91 with the highest COS similarity may yield a lower similarity (0.87) than the perfectly matching state, making it impossible to identify the paper feed motor 91 as the cause of the abnormal noise. To solve this problem, in this embodiment, the accuracy of identifying the cause of the abnormal noise is improved by changing the operating state of at least one of the multiple actuators and obtaining similarity when the image forming operation is repeated.

[0064] Figure 14(c) shows a specific example where the stopping timing of the paper feed motor 91 is shifted. By changing the stopping timing of the paper feed motor 91 from data 3 to data 6, the abnormal noise level also becomes '1' (abnormal noise occurrence state) up to data 7. In this embodiment, the cause of the abnormal noise is identified using these two COS similarity values, which have different stopping timings for the paper feed motor 91.

[0065] Figure 15 is a flowchart showing the procedure for sound measurement processing according to the fourth embodiment. When printing starts, first in S601, the drive control unit 170 determines whether the initial values ​​for the counts of the four types of motors driven during the second measurement have been set. If it is determined that they have not been set (NO in S601), the process proceeds to S602, where the drive control unit 170 sets initial values ​​for the counts of each motor. For example, the initial value for the count of the paper feed motor 91 is set to 6, and the initial value for the count of the intermediate transfer body / K photoreceptor drum motor 92 is set to 4, with initial values ​​shifted by 2 each time.

[0066] In S603, the drive control unit 170 determines whether there is a motor among the four types of motors whose count exceeds a predetermined threshold (8 in this embodiment). If there is a motor whose count exceeds the threshold (YES in S603), the process proceeds to S604, where the drive control unit 170 shifts the stopping timing of that motor. This control shifts the stopping timing of the motor whose count exceeds the predetermined threshold, and the second measurement is performed. Then, in S605, the drive control unit 170 sets the count of that motor to zero. In S606, the drive control unit 170 adds 1 to the counts of the other motors. On the other hand, if there is no motor whose count exceeds the threshold (NO in S603), the process proceeds to S607, where the drive control unit 170 operates each motor at its normal timing. Then, in S608, the drive control unit 170 adds 1 to the counts of all four types of motors. By performing a second measurement using the above drive control, it is possible to alternate between normal measurement and measurement with a modified stop timing for one motor whose count exceeds a threshold.

[0067] The cause identification unit 325 calculates the COS similarity of each motor from the results of normal measurements and measurements with shifted motor stop timings. If the COS similarity of each motor is above a threshold (0.8 in this example), it identifies that motor as the cause of the abnormal noise. In this embodiment, as shown in Figures 14(b) and (c), the COS similarity of the paper feed motor 91 before and after changing the stop timing is 0.8 or higher, so the paper feed motor 91 is identified as the actuator causing the abnormal noise. Furthermore, in the second measurement, the paper feed motor 91 drives the register roller pair 25, so the cause identification unit 325 identifies the register roller pair 25 as the unit causing the abnormal noise.

[0068] As described above, according to the fourth embodiment, the accuracy of cause identification can be improved by identifying the cause from the occurrence of abnormal noise in two states with different motor operating timings. In this embodiment, an example of delaying the motor stop timing is shown, but the motor stop timing may be advanced. Alternatively, the motor start timing, or both the start and stop timings may be changed. Furthermore, the frequency of changing the motor stop timing is not limited to this, and for example, the number of sheets of paper passed through may be counted, and when a predetermined number is reached, the stop timing of one motor may be changed according to a predetermined order.

[0069] <Other Embodiments> In each of the above embodiments, the sound diagnostic unit 320 provided in the server 300 performed the functions, but this is not limited to this. For example, the engine control unit 110 of the printer 100 may perform at least some of the functions of the sound diagnostic unit 320. Alternatively, some of the functions of the received sound processing unit 140 in the engine control unit 110 of the printer 100 (for example, the reference value setting unit 143, the square calculation unit 144, and the interval average calculation unit 145) and the sound wave information processing unit 150 may be provided in the server 300. In this case, the printer 100 transmits information indicating the sound received by the receiving unit 71 (for example, a digital value) to the received sound processing unit provided in the server 300 via the network. The status notification unit 160 also transmits actuator information indicating the operating status to the sound wave information processing unit 150 provided in the server 300 via the network. The received sound processing unit of the server 300 calculates sound wave level data based on the information received from the printer 100, and the sound wave information processing unit 150 generates sound data based on the actuator information received from the status notification unit 160. The sound diagnostic unit 320 determines whether or not an abnormal noise is being generated in the printer 100, or identifies the unit emitting the abnormal noise, based on the generated sound data. As described above, the engine control unit 110 of the printer 100 may perform at least some of the functions of the sound diagnostic unit 320, or the server 300 may perform at least some of the functions of the received sound processing unit 140, etc.

[0070] Furthermore, the cause identification results are communicated by the notification unit 330 to the host computer 200 of the user or dealer, a printer management tool (not shown), etc., but this configuration is not limited to this. For example, the cause identification results may be communicated to the printer's display unit, such as the operation panel included in the operation display unit 102.

[0071] Furthermore, the accuracy of cause identification can be further improved by combining the above-described embodiments. For example, the first and second embodiments may be combined to add cause identification processing for monochrome and full-color operations in addition to the cause identification of abnormal noises during printing operations. Alternatively, the first and third embodiments may be combined to add processing to determine whether the drive unit is the cause of abnormal noises during high-temperature and low-temperature printing operations.

[0072] Furthermore, while an image forming apparatus (printer 100) that performs image forming operations was given as an example of an object to identify the cause of abnormal noise, i.e., an object to abnormal noise diagnosis, it is not limited to this. Any device having multiple drive units and multiple operating units, in which the same sequence of operations is repeatedly executed, can be an object to the abnormal noise diagnosis described in each of the embodiments above.

[0073] 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.

[0074] The disclosures herein include the following abnormal sound diagnostic systems, image forming apparatuses, abnormal sound diagnostic methods, and programs. (Item 1) An abnormal noise diagnostic system for identifying the cause of an abnormal noise in a device having a plurality of operating parts that perform predetermined operations and a plurality of drive parts that drive the plurality of operating parts, A determination means for determining the occurrence of abnormal noise in each of the multiple time intervals based on the sound wave level measured in each of the multiple time intervals of the sound generated within the device, Acquisition means for acquiring the driving state of each of the multiple drive units in each of the multiple time intervals, The system includes a determination means for identifying a drive unit corresponding to the generated abnormal noise from among the plurality of drive units, based on the abnormal noise generation state determined by the determination means and the drive state acquired by the acquisition means. The abnormal noise diagnosis system is characterized in that the identifying means obtains a plurality of comparison results corresponding to the different timings by comparing the drive state and the generation state obtained at different timings of the predetermined operation, and identifies the drive unit corresponding to the generation of abnormal noise based on the plurality of comparison results. (Item 2) The abnormal noise diagnosis system according to item 1, characterized in that the drive state is a sequence of expressions representing whether or not each drive unit is driven in each of the plurality of time intervals, and the occurrence state is a sequence of expressions representing whether or not an abnormal noise occurs in each of the plurality of time intervals. (Item 3) The abnormal noise diagnosis system according to item 1 or 2, characterized in that the identifying means identifies the drive unit corresponding to the generated abnormal noise based on the degree of similarity between the generation state and the drive state. (Item 4) The aforementioned predetermined operation includes an operation related to image formation, The abnormal noise diagnosis system according to any one of items 1 to 3, characterized in that the identifying means obtains a plurality of comparison results corresponding to the different timings by comparing the driving state and the generation state obtained at different timings of the operation related to the image formation, and identifies the driving unit corresponding to the generation of abnormal noise based on the plurality of comparison results. (Item 5) The abnormal sound diagnosis system according to item 4, characterized in that the aforementioned different timings include multiple timings in the image forming operation performed on the recording material. (Item 6) The abnormal noise diagnostic system according to item 4 or 5, characterized in that the aforementioned different timings include the timing during which calibration for correcting toner density is performed in monochrome operation and the timing during which calibration is performed in full-color operation. (Item 7) The device further comprises a detection means for detecting the internal temperature of the device, The abnormal noise diagnostic system according to any one of items 4 to 6, characterized in that the different timings include timings distinguished based on the temperature detected by the detection means. (Item 8) The system further includes drive control means for changing the operating timing of at least one of the plurality of drive units for each image forming operation, The abnormal noise diagnosis system according to any one of items 4 to 7, characterized in that the different timings include predetermined timings in the image forming operation before and after changing the operating timing of at least one of the plurality of drive units. (Item 9) The abnormal noise diagnosis system according to any one of items 1 to 8, characterized in that the determination means collects the measured sound wave levels for each of the plurality of time intervals and performs statistical processing to determine the occurrence of abnormal noise for each time interval. (Item 10) The abnormal noise diagnosis system according to item 9, characterized in that, in the statistical processing, a threshold is set for each time interval based on the collected sound wave levels, and when the statistical value of the most recent predetermined number of sound wave levels is equal to or greater than the threshold, it is determined that an abnormal noise is occurring. (Item 11) The abnormal sound diagnosis system according to item 9 or 10, characterized in that the determination means classifies the sound wave levels measured based on the information of the recording material set with respect to the image forming operation, and performs the statistical processing for each classification. (Item 12) The abnormal noise diagnosis system according to any one of items 9 to 11, characterized in that the determination means classifies the measured sound wave levels into groups in which the driving state of one or more of the multiple driving units matches during the multiple time intervals, and performs the statistical processing for each classification. (Item 13) The abnormal noise diagnosis system according to any one of items 1 to 12, characterized in that the identifying means identifies an operating unit among the plurality of operating units, at least in part, that is driven by the identified drive unit, as the operating unit corresponding to the abnormal noise. (Item 14) The abnormal noise diagnosis system according to any one of items 1 to 13, further comprising notification means for notifying a drive unit identified by the specified means, or an operating unit that is at least partially driven by the identified drive unit. (Item 15) An image forming apparatus having a plurality of operating units for image formation and a plurality of drive units for driving the plurality of operating units, A drive control means for controlling the drive of each of the plurality of drive units, The image forming apparatus includes a detection means for detecting the sound wave level generated, A determination means for determining the occurrence of an abnormal sound in each of the multiple time intervals based on the sound wave level detected by the detection means in each of the multiple time intervals, Acquisition means for acquiring the driving state of each of the multiple drive units in each of the multiple time intervals, The system includes a determination means for identifying a drive unit corresponding to the generated abnormal noise from among the plurality of drive units, based on the abnormal noise generation state determined by the determination means and the drive state acquired by the acquisition means. The image forming apparatus is characterized in that the identifying means obtains a plurality of comparison results corresponding to the different timings by comparing the drive state and the generation state obtained at different timings of the operation related to image forming, and identifies the drive unit corresponding to the generation of abnormal noise based on the plurality of comparison results. (Item 16) A method for diagnosing abnormal noises in a device having a plurality of operating parts that perform predetermined operations and a plurality of drive parts that drive the plurality of operating parts, wherein the cause of the abnormal noise is identified. A determination step of determining the occurrence of abnormal noise in each of the multiple time intervals based on the sound wave level measured in each of the multiple time intervals of the sound generated within the device, An acquisition step to acquire the driving state of each of the multiple drive units in each of the multiple time intervals, The system includes a determination step for identifying the drive unit corresponding to the generated abnormal noise from among the plurality of drive units, based on the abnormal noise generation state determined by the determination step and the drive state acquired by the acquisition step. The method for diagnosing abnormal noise is characterized in that, in the specified step, a plurality of comparison results corresponding to the different timings are obtained by comparing the drive state and the generation state obtained at different timings of the predetermined operation, and the drive unit corresponding to the generation of abnormal noise is identified based on the plurality of comparison results. (Item 17) A program to cause a computer to execute the abnormal noise diagnosis method described in item 16.

[0075] The invention is not limited to the embodiments described above, and various modifications and variations are possible without departing from the spirit and scope of the invention. Accordingly, claims are attached to disclose the scope of the invention. [Explanation of Symbols]

[0076] 100: Printer, 200: Host computer, 300: Server, 101: Video controller, 102: Operation display unit, 103: Printer engine, 104: System bus, 105: IO port, 110: Engine control unit, 140: Received sound processing unit

Claims

1. An abnormal noise diagnostic system for identifying the cause of abnormal noises occurring in an image forming apparatus comprising multiple operating parts and multiple motors that drive the multiple operating parts, A microphone configured to receive sound during multiple periods, which are included in the image forming period in which the image forming apparatus forms an image on a predetermined number of recording materials based on the print data received by the image forming apparatus, and during which at least one of the multiple motors is in operation; A display means configured to display information, Means of acquiring information, The system includes a notification means configured to notify the display means to display first information indicating the cause of the abnormal noise, The plurality of motors include a first motor and a second motor, The plurality of operating units include a first operating unit driven based on the driving force of the first motor and a second operating unit driven based on the driving force of the second motor, The aforementioned plurality of periods include a first period in which the first motor and the second motor are in operation, and a second period in which the second motor is in operation and the first motor is not in operation. An abnormal noise diagnostic system characterized in that, when information indicating the occurrence of the abnormal noise during the first period is defined as first abnormal noise information, and information indicating the occurrence of the abnormal noise during the second period is defined as second abnormal noise information, the acquisition means acquires second information indicating that the second operating unit is more likely than the first operating unit to be the cause of the abnormal noise, based on the first abnormal noise information and the second abnormal noise information.

2. The abnormal noise diagnosis system according to Claim 1, characterized in that the second information is information indicating the degree of similarity between (i) whether or not the abnormal noise occurred in each of the plurality of periods and (ii) whether or not the plurality of motors operated in each of the plurality of periods.

3. The abnormal noise diagnosis system according to claim 2, characterized in that the acquisition means collects the sound wave levels of the abnormal noise over the multiple periods, performs statistical processing, and determines whether or not the abnormal noise occurred in each of the multiple periods.

4. The abnormal noise diagnosis system according to claim 3, characterized in that the statistical processing sets a threshold for each of the multiple periods based on the collected sound wave levels, and the acquisition means determines that the abnormal noise has occurred when a predetermined number of recent statistical values ​​of sound wave levels are equal to or greater than the threshold.

5. The abnormal noise diagnosis system according to claim 3, characterized in that the acquisition means classifies the measured sound wave levels into groups in which the driving state of at least one of the plurality of motors coincides during the plurality of periods, and performs the statistical processing for each classification.

6. The abnormal noise diagnosis system according to claim 1, characterized in that the notification means is configured to notify a motor included in the plurality of motors, or an operating unit included in the plurality of operating units.

7. An image forming apparatus comprising a plurality of operating parts for image formation and a plurality of motors for driving the plurality of operating parts, A microphone configured to receive sound during multiple periods, which are included in the image forming period in which the image forming apparatus forms an image on a predetermined number of recording materials based on the print data received by the image forming apparatus, and during which at least one of the multiple motors is in operation; A display means configured to display information, Means of acquiring information, The display means is configured to notify the image forming apparatus to display first information indicating the cause of an abnormal noise that occurs in the image forming apparatus, The plurality of motors include a first motor and a second motor, The plurality of operating units include a first operating unit driven based on the driving force of the first motor and a second operating unit driven based on the driving force of the second motor, The aforementioned plurality of periods include a first period in which the first motor and the second motor are in operation, and a second period in which the second motor is in operation and the first motor is not in operation. Image forming apparatus characterized in that, when information indicating the occurrence of the abnormal noise during the first period is defined as first abnormal noise information, and information indicating the occurrence of the abnormal noise during the second period is defined as second abnormal noise information, the acquisition means acquires second information indicating that the second operating unit is more likely than the first operating unit to be the cause of the abnormal noise, based on the first abnormal noise information and the second abnormal noise information.

8. A method for diagnosing abnormal noises occurring in an image forming apparatus comprising multiple operating parts and multiple motors that drive the multiple operating parts, wherein the cause of the abnormal noise is identified. A receiving step in which a microphone receives sound during multiple periods that are included in the image forming period in which the image forming apparatus forms an image on a predetermined number of recording materials based on the print data received by the image forming apparatus, and during which at least one of the multiple motors is in operation. A display process in which information is displayed on a display means, The information acquisition process, The system includes a notification step of notifying the display means to display first information indicating the cause of the abnormal noise, The plurality of motors include a first motor and a second motor, The plurality of operating units include a first operating unit driven based on the driving force of the first motor and a second operating unit driven based on the driving force of the second motor, The aforementioned plurality of periods include a first period in which the first motor and the second motor are in operation, and a second period in which the second motor is in operation and the first motor is not in operation. A method for diagnosing abnormal noise, characterized in that, when information indicating the occurrence of the abnormal noise during the first period is defined as first abnormal noise information, and information indicating the occurrence of the abnormal noise during the second period is defined as second abnormal noise information, in the acquisition step, second information is acquired based on the first abnormal noise information and the second abnormal noise information, indicating that the second operating unit is more likely to be the cause of the abnormal noise than the first operating unit.

9. A program for causing a computer to execute a noise diagnosis method for identifying the cause of abnormal noises occurring in an image forming apparatus comprising a plurality of operating parts and a plurality of motors that drive the plurality of operating parts, wherein the noise diagnosis method is: A receiving step in which a microphone receives sound during multiple periods that are included in the image forming period in which the image forming apparatus forms an image on a predetermined number of recording materials based on the print data received by the image forming apparatus, and during which at least one of the multiple motors is in operation. A display process in which information is displayed on a display means, The information acquisition process, The system includes a notification step of notifying the display means to display first information indicating the cause of the abnormal noise, The plurality of motors include a first motor and a second motor, The plurality of operating units include a first operating unit driven based on the driving force of the first motor and a second operating unit driven based on the driving force of the second motor, The aforementioned plurality of periods include a first period in which the first motor and the second motor are in operation, and a second period in which the second motor is in operation and the first motor is not in operation. A program in which, when information indicating the occurrence of the abnormal noise during the first period is defined as first abnormal noise information, and information indicating the occurrence of the abnormal noise during the second period is defined as second abnormal noise information, in the acquisition step, second information is acquired based on the first abnormal noise information and the second abnormal noise information, indicating that the second operating unit is more likely than the first operating unit to be the cause of the abnormal noise.

Images