JP2020070115A - Carrier - Google Patents

Abstract

To provide a carrier adapted to accurately predict a trouble of transport.SOLUTION: A carrier (100) comprises a transport roller (110), a control section (120) and a predicting section (130). The transport roller (110) carries an object-of-transport (S). The control section (120) controls a transport roller (110). The predicting section (130) predicts a measurement result of the transport roller (110) or a transport result of the object-of-transport (S) from a current setting of a transport parameter for carrying the object-of-transport (S) by the transport roller (110) based on a past setting of the transport parameter and a measurement result of the transport roller (110) or transport result of the object-of-transport corresponding to the past setting of the transport parameter.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a transport device.

  A transport device that transports an object to be transported by a transport roller is used for various purposes. For example, the transport device is used as a part of the image forming apparatus. In this case, the image forming apparatus forms an image on the sheet conveyed by the conveying apparatus, further conveys the sheet by the conveying apparatus, and discharges the sheet to the outside.

  For example, it is known to predict a failure of an image forming apparatus including a conveyance device (Patent Document 1). In the image forming apparatus of Patent Document 1, the pulse signal from the encoder for controlling the motor is frequency-analyzed to determine whether or not there is an abnormality based on a predetermined determination criterion.

JP, 2011-186125, A

  If the settings of the image forming apparatus are different, a problem may occur in the image forming apparatus. However, although the image forming apparatus of Patent Document 1 can predict the failure of the component reflected in the pulse signal from the encoder, it cannot predict that the failure will progress according to the setting of the image forming apparatus.

  The present invention has been made in view of the above problems, and an object of the present invention is to provide a transfer device capable of highly accurately predicting a failure in the transfer of an object to be transferred by the transfer device.

  The transport device according to the present invention includes a transport roller, a control unit, and a prediction unit. The transport roller transports an object to be transported. The control unit controls the transport roller. The predicting unit is configured to transport the transport target based on the transport parameter past setting, the transport roller measurement result or the transport target object transport result corresponding to the transport parameter past setting. From the current setting of the transport parameter of, the measurement result of the transport roller or the transport result of the transport object is predicted.

  ADVANTAGE OF THE INVENTION According to this invention, the malfunction of conveyance of the conveyance target by a conveyance apparatus can be estimated with high precision.

FIG. 3 is a schematic diagram of an image forming apparatus including the transport device of the present embodiment. It is a block diagram of the conveyance apparatus of this embodiment. It is a table which shows the teacher data containing the past setting and a result of a conveyance parameter, and the present setting and a prediction result of a conveyance parameter. It is a table which shows an example of teacher data. It is a table which shows another example of teacher data. FIG. 3 is a schematic diagram of an image forming apparatus including the transport device according to the present exemplary embodiment. It is a block diagram of the conveyance apparatus of this embodiment. It is a flow chart for explaining machine learning processing in a conveyance machine of this embodiment. It is a flow chart for explaining the setting change processing in the conveyance apparatus of the present embodiment.

  Embodiments of the present invention will be described below with reference to the drawings. In the drawings, the same or corresponding parts will be denoted by the same reference symbols and description thereof will not be repeated.

  First, with reference to FIG. 1, the configuration of an image forming apparatus 200 including the carrying device 100 of the present embodiment will be described. FIG. 1 is a schematic diagram of an image forming apparatus 200 including a conveyance device 100. The conveyance device 100 conveys the sheet S as a conveyance target. The image forming apparatus 200 forms an image on the sheet S. The image forming apparatus 200 is, for example, a printer, a copy machine, or a multifunction machine. The image forming apparatus 200 may have a facsimile function. Here, the image forming apparatus 200 is an electrophotographic system.

  The transport device 100 includes a transport roller 110, a control unit 120, and a prediction unit 130. The transport roller 110 transports the sheet S. The transport roller 110 transports the sheet S according to the set transport parameters.

  The transport roller 110 includes a rotating roller. The rotating roller rotates around the rotation axis. The transport roller 110 typically includes a pair of rotating rollers. The pair of rotating rollers face each other and rotate about a rotation axis. In one example, one of the pair of rotating rollers rotates following the other. The sheet S enters between a pair of rotating rotating rollers, is urged by the rotating rollers, and is pushed out from the rotating rollers.

  The transport device 100 includes a plurality of transport rollers 110. In the transport device 100, the transport path for the sheet S is formed by the plurality of transport rollers 110. For example, the sheet S is plain paper, recycled paper, thin paper, thick paper, coated paper, or OHP (Overhead Projector) sheet.

  The control unit 120 controls the transport roller 110. For example, the controller 120 controls the timing and the number of rotations of the transport roller 110. Further, the control unit 120 controls the conveyance speed of the sheets S and the interval between the sheets S that are continuously conveyed.

  The control unit 120 includes a computing element. The computing element includes a processor. In one example, the processor includes a central processing unit (CPU). The processor may include an application specific integrated circuit (ASIC).

  The prediction unit 130 predicts the measurement result of the transport roller 110. For example, the prediction unit 130 predicts the diameter of the transport roller 110 without directly measuring the diameter of the transport roller 110.

  Alternatively, the prediction unit 130 predicts the transportation result of the sheet S. For example, the prediction unit 130 predicts at least one of the delay rate of the conveyed sheet S and the delay time of the sheet S.

  The carrier device 100 further includes a storage unit 122. The storage unit 122 includes a storage element. The storage unit 122 may include a memory such as a semiconductor memory. The storage unit 122 includes a main storage element such as a semiconductor memory and an auxiliary storage element such as a semiconductor memory and / or a hard disk drive. The storage unit 122 may include removable media.

  The storage unit 122 stores various data. For example, the storage unit 122 stores a control program. The control unit 120 controls the operation of the transport device 100 by executing the control program. Specifically, the processor of the control unit 120 executes the computer program stored in the storage element of the storage unit 122 to control each component of the transport device 100. For example, the prediction unit 130 is implemented by the control unit 120 executing a computer program.

  For example, the computer program is stored on a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium includes a read only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a magnetic disk or an optical data storage device.

  The storage unit 122 stores the past setting of the transport parameters in the transport device 100. For example, the transport parameters include at least the past settings regarding the temperature inside the transport device 100, the transport speed of the sheet S, the type of the sheet S, and the number of transports of the sheet S. The transport parameters may further include past settings for the hardness of the sheet S and the pressure of the transport roller 110 against the sheet S.

  The storage unit 122 may store the past setting of the transport parameter and the measurement result of the transport roller 110 corresponding to the past setting of the transport parameter. The transport roller 110 may be measured by a measuring member arranged inside the transport device 100 when the transport device 100 is driven. Alternatively, the transport roller 110 may be measured by an external measuring member when the transport device 100 is stopped.

  For example, the storage unit 122 stores the past setting of the transport parameter and the measurement result of the diameter of the transport roller 110 corresponding to the past setting of the transport parameter. The diameter of the transport roller 110 may be regularly measured after the driving of the transport device 100 is stopped. Alternatively, the diameter of the transport roller 110 may be measured when a malfunction occurs in the transport device 100 or the image forming apparatus 200. Alternatively, the diameter of the transport roller 110 may be measured when the unit of the transport device 100 or the image forming apparatus 200 is replaced.

  The storage unit 122 may store the past setting of the transport parameter and the transport result of the sheet S by the transport roller 110 corresponding to the past setting of the transport parameter. For example, the storage unit 122 may store, in addition to the past setting of the transport parameter, the delay rate and the delay time of the sheet S corresponding to the past setting of the transport parameter.

  For example, the prediction unit 130 predicts the measurement result of the transport roller 110 or the transport result of the sheet S based on the data stored in the storage unit 122. Specifically, the prediction unit 130 conveys the conveyance parameter based on the past setting of the conveyance parameter stored in the storage unit 122, the measurement result of the conveyance roller 110 or the conveyance result of the sheet S corresponding to the past setting of the conveyance parameter. The measurement result of the transport roller 110 or the transport result of the sheet S is predicted from the current setting of the transport parameter for the roller 110 to transport the sheet S.

  The carrier device 100 further includes a temperature measuring unit 140. The temperature measuring unit 140 measures the temperature inside the transport device 100. For example, the temperature measuring unit 140 includes a thermistor. The thermistor senses temperature by the change in electrical resistance.

  The transport device 100 further includes a sensor 142. The sensor 142 detects the sheet S transported by the transport roller 110.

  For example, the prediction unit 130 predicts the life of the transport roller 110. The prediction unit 130 may directly predict the life of the transport roller 110. Alternatively, the prediction unit 130 may indirectly predict the life of the transport roller 110.

  For example, the temperature, the transport speed, the smoothness, the hardness, the number of transported sheets, and the pressure of the transport device 100 greatly affect the life of the transport roller 110. When the transport roller 110 is greatly worn, a problem occurs in transporting an object to be transported by the transport roller 110, and the transport roller 110 reaches the end of its life. This is because the degree of wear of the transport roller 110 changes depending on the temperature, the transport speed, the smoothness, the hardness, the number of transported sheets, and the pressure. Typically, when the transport roller 110 has a diameter of 30 mm when mounted on the transport device 100, the transport roller 110 may be worn to a diameter of 29.5 mm, which may cause a problem in transport of the transport target object. ..

  Specifically, the lower the temperature of the transport device 100, the more likely the irregularity due to the transport roller 110 occurs. This is because when the temperature is low, the transport roller 110 becomes hard and the friction of the sheet S with respect to the transport roller 110 increases.

  Further, as the conveyance speed of the sheet S is higher, non-specificity due to the conveyance roller 110 is more likely to occur. This is because the sheet S is easily worn on the transport roller 110 when the transport speed is high.

  Typically, the sheet conveyance speed in the image forming apparatus 200 is set according to the print settings of the user. For example, the transport speed is set to 400 mm / s in the plain paper print mode, and the transport speed is set to 200 mm / s, which is half the plain paper print mode in the thick paper print mode. In this case, the plain paper print mode is more easily worn than the thick paper print mode.

  Further, the more slippery the sheet S is, the more likely the non-specificity due to the transport roller 110 occurs. This is because when the sheet S has high smoothness, the transport roller 110 is easily worn.

  Further, the harder the sheet S, the more likely the non-specificity due to the transport roller 110 occurs. This is because the transport roller 110 is easily worn when the sheet S is hard.

  Further, as the number of conveyed sheets S is larger, non-specificity due to the conveyance rollers 110 is more likely to occur. This is because the transport roller 110 is easily worn when the number of sheets S transported is large.

  Further, the higher the pressure of the transport roller 110 with respect to the sheet S, the more likely the non-specificity due to the transport roller 110 occurs. This is because when the pressure of the transport roller 110 on the sheet S is high, the transport roller 110 is easily worn. In addition, typically, in the same conveying apparatus 100, the setting of the pressure of the conveying roller 110 with respect to the sheet S is not changed. The pressure of the transport roller 110 with respect to the sheet S varies depending on the transport device and / or the image forming device of different models.

  As described above, the prediction unit 130 may predict the diameter of the transport roller 110. Alternatively, the prediction unit 130 may predict the delay rate and the delay time of the sheet S by the transport roller 110.

  The image forming apparatus 200 includes a feeding unit 210, an image forming unit 220, and a device control unit 230 in addition to the transport device 100. The feeding unit 210 stores the sheet S and feeds the sheet S. The image forming unit 220 forms an image on the sheet S conveyed by the conveying device 100. The device control unit 230 controls the feeding unit 210 and the image forming unit 220.

  The feeding unit 210 includes a cassette 212 and a feeding roller 214. The cassette 212 accommodates a plurality of sheets S. The feeding roller 214 feeds the sheet S stored in the cassette 212. The feeding roller 214 feeds the uppermost sheet S of the plurality of sheets S stored in the cassette 212 one by one. Here, the feeding unit 210 includes a plurality of cassettes 212, and a feeding roller 214 is installed for each of the plurality of cassettes 212.

  The transport device 100 transports the sheet S to the image forming unit 220. Specifically, the conveyance device 100 supplies the sheets S fed by the feeding unit 210 to the image forming unit 220 one by one.

  The transport device 100 transports the sheet S from the feeding unit 210 to the discharging unit via the image forming unit 220. The sheet S is ejected from the feeding unit 210, the conveyance device 100, and the image forming unit 220.

  The sensor 142 includes a first sensor 142a and a second sensor 142b. The first sensor 142a is located downstream of the feeding roller 214. In addition, the first sensor 142a is located downstream of the most upstream transport roller 110. The second sensor 142b is located upstream of the transport roller 110 immediately before the image forming unit 220 among the plurality of transport rollers 110.

  The first sensor 142a detects the sheet S. The first sensor 142a detects the sheet S fed by the feeding roller 214. The first sensor 142a detects the sheet S when the leading edge of the conveyed sheet S reaches the detection position of the first sensor 142a. The first sensor 142a may detect the sheet S without contacting the sheet S. For example, the first sensor 142a includes a photo sensor. Alternatively, the first sensor 142a may detect the sheet S by contacting or colliding with the sheet S. For example, the first sensor 142a includes an actuator.

  The second sensor 142b detects the sheet S. The second sensor 142b detects the sheet S transported by the transport roller 110. The second sensor 142b detects the sheet S when the leading edge of the conveyed sheet S reaches the detection position of the second sensor 142b. The second sensor 142b may detect the sheet S without contacting the sheet S. For example, the second sensor 142b includes a photo sensor. Alternatively, the second sensor 142b may detect the sheet S by contacting or colliding with the sheet S. For example, the second sensor 142b includes an actuator.

  The transport roller 110 includes a registration roller 110r. The registration roller 110r is located downstream of the second sensor 142b. The registration roller 110r adjusts the timing of conveying the sheet S to the image forming unit 220. The registration roller 110r temporarily stops the conveyance of the sheet S, and conveys the sheet S to the image forming unit 220 at a predetermined timing of the image forming unit 220.

  The toner containers Ca to Cd are attached to the image forming apparatus 200. Each of the toner containers Ca to Cd is detachable from the image forming apparatus 200. Toners of different colors are stored in the toner containers Ca to Cd, respectively. The toner in the toner containers Ca to Cd is supplied to the image forming unit 220. The image forming unit 220 forms an image using the toner supplied from the toner containers Ca to Cd.

  For example, the toner container Ca stores yellow toner and supplies the yellow toner to the image forming unit 220. The toner container Cb contains magenta toner and supplies the image forming unit 220 with magenta toner. The toner container Cc contains cyan toner and supplies cyan toner to the image forming unit 220. The toner container Cd contains black toner, and supplies the black toner to the image forming unit 220.

  The image forming unit 220 forms an image based on the image data on the sheet S using the toner contained in the toner containers Ca to Cd. Here, the image forming unit 220 includes an exposure unit 221, a photosensitive drum 222a, a charging unit 222b, a developing unit 222c, a primary transfer roller 222d, a cleaning unit 222e, an intermediate transfer belt 223, a secondary transfer roller 224, and a fixing unit. The part 225 is included.

  The photoconductor drum 222a, the charging unit 222b, the developing unit 222c, the primary transfer roller 222d, and the cleaning unit 222e are provided corresponding to each of the toner containers Ca to Cd. The plurality of photosensitive drums 222 a are in contact with the outer surface of the intermediate transfer belt 223 and are arranged along the rotation direction of the intermediate transfer belt 223. The plurality of primary transfer rollers 222d are provided corresponding to the plurality of photosensitive drums 222a. The plurality of primary transfer rollers 222d face the plurality of photosensitive drums 222a via the intermediate transfer belt 223.

  The charging unit 222b charges the peripheral surface of the photosensitive drum 222a. The exposure unit 221 irradiates each of the photosensitive drums 222a with light based on image data, and an electrostatic latent image is formed on the peripheral surface of the photosensitive drums 222a. The developing unit 222c attaches toner to the electrostatic latent image to develop the electrostatic latent image, and forms a toner image on the peripheral surface of the photosensitive drum 222a. Therefore, the photosensitive drum 222a carries a toner image. The primary transfer roller 222d transfers the toner image formed on the photosensitive drum 222a to the outer surface of the intermediate transfer belt 223. The cleaning unit 222e removes the toner remaining on the peripheral surface of the photosensitive drum 222a.

  The photoconductor drum 222a corresponding to the toner container Ca forms a yellow toner image based on the electrostatic latent image, and the photoconductor drum 222a corresponding to the toner container Cb forms a magenta toner image based on the electrostatic latent image. To form. The photoconductor drum 222a corresponding to the toner container Cc forms a cyan toner image based on the electrostatic latent image, and the photoconductor drum 222a corresponding to the toner container Cd forms a black toner image based on the electrostatic latent image. To form.

  On the outer surface of the intermediate transfer belt 223, toner images of a plurality of colors are superimposed and transferred from the photosensitive drum 222a, and an image is formed. Therefore, the intermediate transfer belt 223 carries an image. The secondary transfer roller 224 transfers the image formed on the outer surface of the intermediate transfer belt 223 to the sheet S.

  The fixing unit 225 fixes the toner image on the sheet S by heating and pressing the sheet S on which the toner image is transferred. The fixing unit 225 includes a heating roller 225a and a pressure roller 225b. The heating roller 225a and the pressure roller 225b are arranged to face each other and form a fixing nip. The sheet S that has passed between the intermediate transfer belt 223 and the secondary transfer roller 224 is pressurized while being heated at a predetermined fixing temperature by passing through the fixing nip. As a result, the toner image is fixed on the sheet S.

  The transport device 100 discharges the sheet S on which the image is formed by the image forming unit 220 to the outside of the image forming device 200.

  The device control unit 230 controls the operation of the image forming apparatus 200. Specifically, the device control unit 230 controls the feeding unit 210, the image forming unit 220, and the device storage unit 232.

  The device controller 230 includes a computing element. The computing element includes a processor. In one example, the processor includes a central processing unit (CPU). The processor may include an application specific integrated circuit (ASIC).

  The device storage unit 232 includes a storage element. The device storage unit 232 may include a memory such as a semiconductor memory. The device storage unit 232 includes a main storage element such as a semiconductor memory and an auxiliary storage element such as a semiconductor memory and / or a hard disk drive. The device storage unit 232 may include removable media.

  The device control unit 230 stores information in the device storage unit 232. The device control unit 230 also reads information from the device storage unit 232.

  The device storage unit 232 stores various data. For example, the device storage unit 232 stores a control program. The device control unit 230 controls the operation of the image forming apparatus 200 by executing a control program. Specifically, the processor of the device control unit 230 executes the computer program stored in the storage element of the device storage unit 232 to execute each configuration of the image forming apparatus 200 (for example, the feeding unit 210 and the image forming unit 220. ) Control.

  For example, the computer program is stored on a non-transitory computer-readable storage medium. Non-transitory computer readable storage media include ROM, RAM, CD-ROM, magnetic tape, magnetic disk or optical data storage.

  The image forming apparatus 200 may further include a display unit 240. The display unit 240 can display various images. The display unit 240 may include a liquid crystal display.

  The image forming apparatus 200 may further include an input unit 250. A user operation is input to the input unit 250. The input unit 250 may be provided integrally with the display unit 240. The input unit 250 includes a touch panel. The touch panel receives an input operation from the user by detecting the touch of the user.

  To the input unit 250, the setting of the transport parameters for transporting the sheet S is input. In one example, the display unit 240 displays default transfer parameter settings, and the user inputs the transfer parameter settings to the input unit 250 to change the transfer parameter settings. Alternatively, the user may input the default transport parameter settings via the input unit 250 by starting the print start button without changing the transport parameter settings.

  For example, the information indicating the type of the sheet S is input to the input unit 250. Alternatively, information indicating the number of conveyed sheets S is input to the input unit 250.

  The image forming apparatus 200 may further include an audio output unit 252. The voice output unit 252 outputs voice to the user under the control of the device control unit 230.

  The image forming apparatus 200 may further include a communication unit 254. The communication unit 254 transmits information or data to an external server and receives information or data from the external server. For example, the communication unit 254 may transmit the data stored in the storage unit 122 to the external server and receive the processing result of the external server.

  The image forming apparatus 200 may further include a reading device 260. The reading device 260 includes a document feeding unit 262 and a reading unit 264. The original document R is placed on the original document transport unit 262. The document transport unit 262 transports the document R to the reading unit 264. The reading unit 264 reads a document image formed on the document R and generates image data representing the document image. The device control unit 230 acquires image data representing a document image from the reading unit 264 and causes the device storage unit 232 to store the image data.

  The device control unit 230 may be configured integrally with the control unit 120. For example, the device control unit 230 may include the control unit 120.

  The device storage unit 232 may be configured integrally with the storage unit 122. For example, the device storage unit 232 may include the storage unit 122.

  Next, the configuration of the transport device 100 will be described with reference to FIGS. 1 and 2. FIG. 2 is a block diagram of the transport device 100 of this embodiment.

  The control unit 120 controls the transport roller 110. The conveyance roller 110 starts rotation at a predetermined timing based on a signal from the control unit 120 and conveys the sheet S. Further, the transport roller 110 rotates at a predetermined rotation speed and transports the sheet S at a predetermined transport speed based on a signal from the control unit 120.

  The control unit 120 controls the temperature measuring unit 140. The temperature measuring unit 140 measures the temperature inside the transport device 100. The measurement result measured by the temperature measuring unit 140 is stored in the storage unit 122. The control unit 120 may constantly monitor the measurement result of the temperature measuring unit 140, or may regularly monitor it.

  The control unit 120 controls the sensor 142. When the sensor 142 detects the sheet S being conveyed, the sensor 142 transmits a detection signal to the control unit 120. The control unit 120 detects one of the presence or absence of the sheet S, the conveyance speed of the sheet S, and the number of conveyed sheets S based on the detection result of the sensor 142.

  The storage unit 122 stores the past setting of the transport parameters. The transport parameters include the temperature measured by the temperature measuring unit 140, the transport speed of the sheet S, the type of the sheet S, and the number of transports of the sheet S by the transport roller 110. Further, the transport parameter may further include the hardness of the sheet S and the pressure of the transport roller 110 with respect to the sheet S.

  The prediction unit 130 uses the past setting of the transport parameters stored in the storage unit 122 as teacher data. The prediction unit 130 predicts the measurement result or the transportation result by machine learning processing from the past setting of the transportation parameter.

  Next, a prediction method by the prediction unit 130 will be described with reference to FIGS. FIG. 3 is a schematic table for explaining the prediction of the result for the current setting of the transport parameter from the teacher data including the past setting of the transport parameter and the result. Here, the result indicates the measurement result of the transport roller 110a or the transport result of the sheet S. Further, here, the transport parameters are parameter 1, parameter 2 and parameter 3.

  For example, the teacher data in FIG. 3 shows that the result was Ya when the values of parameter 1, parameter 2 and parameter 3 were X1a, X2a and X3a, respectively. It also shows that the result was Yb when the values of parameter 1, parameter 2 and parameter 3 were X1b, X2b and X3b, respectively. Further, it indicates that the result was Yc when the values of Parameter 1, Parameter 2 and Parameter 3 were X1c, X2c and X3c, respectively.

  In the transport device 100 of the present embodiment, the prediction unit 130 predicts the result Yr when the current set values of the parameter 1, the parameter 2 and the parameter 3 are X1r, X2r and X3r, respectively. The prediction unit 130 typically uses the past teacher data based on the past teacher data even when there is no result when the combination of the values of the parameter 1, the parameter 2, and the parameter 3 is X1r, X2r, and X3r. , Predict the result Yr when the values of Parameter 1, Parameter 2 and Parameter 3 are X1r, X2r and X3r, respectively.

  In the transport device 100 of the present embodiment, the prediction unit 130 preferably derives a prediction formula for predicting the result Yr from the transport parameter values X1r, X2r, and X3r. The prediction formula can be derived from the transport parameter values X1a to X3a, X1b to X3b, and X1c to X3c corresponding to the results Ya to Yc. In this case, the transport parameter values X1a to X3a, X1b to X3b, X1c to X3c and the results Ya to Yc are used as teacher data. For example, the prediction unit 130 may derive the prediction formula by machine learning processing using the transport parameter values X1a to X3a, X1b to X3b, X1c to X3c and the results Ya to Yc as teacher data.

  In addition, in FIG. 3, there are three transfer parameters as the teacher data, but the present embodiment is not limited to this. It is preferable to use four or more transfer parameters as teacher data. In addition, the transport parameter is preferably an item that is strongly related to the result. For example, the transport parameters preferably include at least one of temperature, transport speed, smoothness, hardness, number of transported sheets, and pressure.

  Further, although the number of teacher data is three in FIG. 3, the present embodiment is not limited to this. The number of teacher data may be four or more. For example, the number of pieces of teacher data may be 10 or more, 100 or more, or 1000 or more.

  When the number of teacher data is large, the teacher data may be separated into learning data and test data to derive the prediction formula. The learning data is used to estimate the prediction formula, and the test data is used to verify the estimated prediction formula. If the number of teacher data is large, the learning data and the test data may be randomly separated from the teacher data.

  Further, when the machine learning process is performed using the teacher data or the learning data, the teacher data or the learning data is preferably normalized. For example, the normalization process is performed using Z-score.

  The control unit 120 may warn the user based on the prediction result of the prediction unit 130. For example, when the result Yr is significantly different from the initial prediction, the control unit 120 may warn the user based on the prediction result of the prediction unit 130. In one example, the control unit 120 controls the display unit 240 so that the display unit 240 displays a screen that prompts a change necessary to extend the life of the transport roller 110. In this case, the display unit 240 displays a screen showing information related to the transport parameter that can be changed by the user among the plurality of transport parameters.

  For example, the display unit 240 displays information that prompts a change in the temperature inside the image forming apparatus 200. In one example, the warning displayed by the display unit 240 may include a request to change the air conditioning of the space where the image forming apparatus 200 is installed and / or a request to move the installation location of the image forming apparatus 200.

  Alternatively, the display unit 240 displays information prompting the change of the type of the sheet S. In one example, the warning displayed by the display unit 240 may include a request to change the sheet S.

  In addition, the control unit 120 controls the voice output unit 252 so that the voice output unit 252 gives a voice warning of information urging a change necessary to extend the life of the transport roller 110. For example, the voice output unit 252 gives a voice warning of information prompting a change in the temperature inside the image forming apparatus 200. Alternatively, the voice output unit 252 gives a voice warning of information prompting the change of the type of the sheet S.

  The control unit 120 may also transmit information indicating a warning to the information processing terminal (not shown) of the user via the communication unit 254. Alternatively, the control unit 120 may transmit the information indicating the warning to the administrator of the image forming apparatus 200 or the management company via the communication unit 254.

  The control unit 120 may compare the result previously predicted by the prediction unit 130 and the result predicted this time by the prediction unit 130, and may warn the user based on the compared result. For example, the control unit 120 may warn the user when the result predicted this time by the prediction unit 130 is significantly different from the result predicted previously by the prediction unit 130. In one example, when the prediction result of this time is significantly different from the previous prediction result by the prediction unit 130 with respect to the number of conveyed sheets, the control unit 120 controls the display unit 240 and / or the voice output unit 252 to warn the user. To do.

  The control unit 120 may warn a conveyance parameter having a setting that is significantly different from the past setting among the plurality of conveyance parameters. For example, when the prediction result is significantly changed as a result of the type of the sheet S being changed immediately before, the control unit 120 may warn the user to restore the type of the sheet S to the original. Further, the image forming apparatus 200 may detect the type of the sheet S by the media sensor, and when the prediction result is significantly changed, the control unit 120 may warn the user to return the type of the sheet S to the original.

  As mentioned above, the warning informs the user that this may shorten the life. Based on the warning, the user can determine whether to change the environment and / or the type of the sheet S so as to prolong the life. For example, when the user prioritizes the extension of the life, the life of the transport roller 110 can be extended by changing the environment and / or the type of the sheet S based on the warning. On the other hand, when the user prioritizes maintaining the existing environment and the type of the sheet S over the life, the conveyance and the image formation may be continued under the same environment regardless of the warning.

  Alternatively, the user may approve in advance to automatically make the necessary setting changes in order to prolong the life if the prediction result changes significantly. When the user approves in this way, the control unit 120 may automatically change the current setting of the transport parameter based on the change in the prediction result.

  Data collected in the development of the transport device 100 and / or the image forming apparatus 200 may be used as the teacher data. Alternatively, the teacher data may be collected while the transport device 100 and / or the image forming device 200 is being driven. Alternatively, the teacher data may include data collected while driving another conveyance device and / or an image forming apparatus having a similar configuration. Alternatively, the teacher data may include data collected during development, data collected during driving, and / or data collected while driving other transport devices and / or image forming devices.

  The teacher data may be divided into learning data and test data. For example, the teacher data may be randomly divided into learning data and test data.

  Note that FIG. 3 shows a table using three parameters as the transport parameters, but the number of transport parameters is not limited to three. The number of transport parameters may be two or more. However, typically, the greater the number of transport parameters, the more accurate the prediction by the prediction unit 130 can be.

  For example, it is preferable to use a parameter related to the result as the transportation parameter. For example, when predicting the diameter of the transport roller 110 or the transport result by the transport roller 110, it is preferable to use the average temperature, the average speed, the average smoothness, and the number of transported sheets as the transport parameters.

  Next, with reference to FIGS. 1, 2 and 4, an embodiment will be described in which the average temperature, the average speed, the average smoothness, the average Young's modulus, the number of conveyed sheets, and the pressure are used as the conveying parameters. FIG. 4 is a table showing an example of teacher data. Here, the transfer parameters in the teacher data are average temperature, average speed, average smoothness, average Young's modulus, number of transferred sheets, and pressure.

  The unit of average temperature is ° C. The average temperature is obtained by calculating the average temperature of the total number of conveyed sheets with reference to the temperature measured by the temperature measuring unit 140 at the timing of conveying the sheets S.

  The unit of the average speed is mm / sec. The average speed is obtained by referring to the transfer speed information at the timing of transferring the sheet S and calculating the average transfer speed for the total number of conveyed sheets.

  The unit of average smoothness is seconds. The smoothness indicates Beck's smoothness. Beck's smoothness indicates average unevenness on the sheet surface. The average smoothness is obtained by calculating the average smoothness of the total number of conveyed sheets with reference to the type of sheet designated at the timing of conveying the sheets S.

Typically, newsprint (density 43 g / m ² ) has a smoothness of about 40 seconds to about 50 seconds, and fine paper (density 64 g / m ² ) has a smoothness of about 50 seconds to about 100 seconds. Is. The smoothness of the lightly coated paper (density 64 g / m ² ) is about 500 seconds to about 1000 seconds, and the smoothness of the lightweight coated paper A (density 364 g / m ² ) is about 1000 seconds to about 2500 seconds. Seconds. Further, the smoothness of coated paper A (density 2127.9 g / m ² ) is about 2000 seconds to about 5000 seconds, and the smoothness of art paper A (density 1127.9 g / m ² ) is about 3000 seconds. It is about 8000 seconds.

  The unit of the average Young's modulus is MPa. The average Young's modulus is obtained by referring to the designated Young's modulus of the sheet S at the timing of conveying the sheet S and calculating the average Young's modulus in the total number of conveyed sheets.

  The unit of the number of conveyed sheets is the number of sheets. The number of conveyed sheets is counted every time the sheet S is conveyed.

  The unit of pressure is N. Here, the pressure indicates the pressure applied to the sheet S by the transport roller 110. Typically, the pressure is set differently depending on the configuration of the image forming apparatus 200.

The derivation of the prediction formula by the machine learning process using the six transport parameters will be described below. The prediction formula is obtained using the prediction function fθ. Here, a prediction function fθ using seven parameters θ _{0 to} θ ₆ is used. The prediction function fθ is expressed as follows.

Here, x _{1 to} x ₆ are input data. Teacher data or learning data is input to x _{1 to} x ₆ . Further, θ _{0 to} θ ₆ are parameters. θ _{0 to} θ ₆ are obtained by machine learning using teacher data or learning data.

In the machine learning process, the parameters θ ₀ to θ ₆ are obtained so that the loss function E (θ) is minimized. When using the least squares method, the loss function E (θ) is expressed as follows. Here, y is input data. Teacher data or learning data is input to y.

The parameters θ ₀ to θ ₆ are changed so that the loss function E (θ) is minimized. The parameters θ _{0 to} θ ₆ are obtained by the steepest descent method using the following parameter updating formula.

Here, j indicates 1 to 6, which is the number of transport parameters. The parameters θ _{0 to} θ ₆ are obtained as described above. As a result, a prediction formula in which the parameters θ ₀ to θ ₆ are input to the prediction function fθ is derived. The prediction result is obtained by inputting x ₁ to x ₆ as the current setting of the transport parameter in the prediction formula.

  Note that, as described above, it is preferable to warn the user when the prediction result changes abruptly. For example, when the diameter of the transport roller 110 changes abruptly, the user is warned. In one example, a warning is issued when the diameter of the transport roller 110 changes by 0.3 mm or more.

  The prediction function fθ may not be updated every time the transport roller 110 transports the sheet S. For example, the prediction function fθ is preferably determined in advance before shipping. However, the prediction function fθ may be periodically updated based on the transportation driven after shipping.

  In the table shown in FIG. 4, the average temperature, the average speed, the average smoothness, the average Young's modulus, the number of conveyed sheets, and the pressure are used as the conveying parameters, but the present embodiment is not limited to this. Regarding the transport parameters, some of the above may be omitted.

  Next, with reference to FIG. 5, an embodiment using the average temperature, the average speed, the average smoothness, and the number of conveyed sheets as the conveying parameters will be described. FIG. 5 is a table showing an example of teacher data. Here, the transfer parameters in the teacher data are average temperature, average speed, average smoothness, and the number of transferred sheets.

  In the table of FIG. 5, compared with the table of FIG. 4, the average Young's modulus and the pressure are omitted from the transport parameters. The average Young's modulus is considered to show the same tendency as the average smoothness. Further, as described above, the pressure is often constant if the image forming apparatus 200 has the same configuration. Therefore, by reducing the number of transport parameters, the calculation amount in the machine learning process may be reduced.

  In the above description with reference to FIGS. 1 to 3, the storage unit 122 of the transport apparatus 100 stores the teacher data and the prediction unit 130 stores the teacher data in order to prevent the description of the invention from becoming too complicated. Although the measurement result and / or the conveyance result are predicted using the teacher data of the unit 122, the present embodiment is not limited to this.

  The communication unit 254 may transmit the teacher data in the storage unit 122 to the external server and receive the result processed by the external server. In this case, the prediction unit 130 may use the result processed by the external server as the prediction result. Alternatively, after the communication unit 254 receives a signal indicating the firmware including the prediction formula from the external server to update the firmware, the prediction unit 130 predicts the result using the updated prediction formula. Good.

  Further, the teacher data may not necessarily be the result of the transport device 100. The teacher data may be the result of another transport device having the same configuration as the transport device 100. Alternatively, the teacher data may include both the result of the transport apparatus 100 and the result of another transport apparatus having the same type of configuration as the transport apparatus 100.

  In the above description with reference to FIG. 1 and FIG. 2, while the temperature measuring unit 140 measures the temperature inside the transfer device 100, the temperature of the transfer device 100 is not adjusted, but the present embodiment is not limited to this. .. The transport device 100 may include a heating member or a cooling member for adjusting the temperature of the transport roller 110.

  Next, with reference to FIG. 6 and FIG. 7, an image forming apparatus 200 including the carrying device 100 of this embodiment will be described. FIG. 6 is a schematic diagram for explaining an image forming apparatus 200 including the carrying device 100 of this embodiment. FIG. 7 is a block diagram of the transport device 100 of this embodiment. The image forming apparatus 200 shown in FIG. 6 is the same as the image forming apparatus described above with reference to FIG. 1 except that the image forming apparatus 200 further includes a heater 112, a fan 144, and a cassette heater 216. Further, the carrier device 100 shown in FIG. 7 is the same as the carrier device described above with reference to FIG. 2 except that the control unit 120 controls the heater 112 and the fan 144. Therefore, redundant description is omitted for the purpose of avoiding redundancy.

  The transfer device 100 of this embodiment further includes a heater 112 and a fan 144. The heater 112 is embedded inside the transport roller 110. The temperature of the transport roller 110 can be raised by the heater 112 heating the transport roller 110. Therefore, the heater 112 can reduce the abrasion of the transport roller 110.

  Further, the fan 144 cools the transport path of the transport device 100. The fan 144 can reduce the temperature inside the transport device 100. Therefore, the abrasion of the transport roller 110 can be reduced by stopping the cooling of the transport roller 110 by the fan 144.

  The feeding unit 210 further includes a cassette heater 216. The cassette heater 216 heats the sheet S in the cassette 212 to remove the moisture absorbed by the sheet S. The cassette heater 216 is arranged so as to cover the entire lower area of the sheet S having the maximum size (for example, A4 size). In addition, the cassette 212 may be provided with a size detection unit that detects the size of the sheets S stored in the cassette 212.

  Next, a machine learning process in the transport device 100 of the present embodiment will be described with reference to FIGS. 1, 2, and 6 to 8. FIG. 8 is a flowchart for explaining machine learning processing in the transport device 100 of this embodiment. A prediction formula for obtaining a prediction result can be derived by the machine learning process. The prediction formula may be derived by the machine learning process by the prediction unit 130. Alternatively, the prediction formula may be derived by the machine learning process by an external server, and the prediction unit 130 may obtain the prediction result based on the prediction formula derived by the external server.

  When the machine learning process is started, first, in step S802, the teacher data is separated into learning data and test data. The teacher data is preferably randomly separated into learning data and test data.

  In step S804, the learning data is normalized. For example, normalization is performed using Z-Score. For example, the normalized data Z obtained by normalizing the original data x is represented by Z = (x-average value) / standard deviation.

  In step S806, the parameter θ is randomly initialized. Thereby, the prediction formula of the initialization state is obtained.

  In step S808, the parameter θ is updated. For example, the parameter θ is updated using the steepest descent method.

  In step S810, an error is calculated based on the learning data. For example, the error is calculated by comparing the calculation result of the prediction formula with the past measurement result or the conveyance result.

  In step S812, it is determined whether the learning function is a certain number or more and the loss function E (θ) has converged. When the number of times of learning is not less than a certain number and the convergence of the loss function E (θ) is not satisfied (No in step S812), the process returns to step S808.

  On the other hand, when the number of learnings is a certain number or more and the loss function E (θ) converges (Yes in step S812), the prediction formula is estimated. The process proceeds to step S814.

  In step S814, the test data is normalized. Typically, the normalization of the test data is performed by the same method as the normalization of the learning data in step S804. The process proceeds to step S816.

  In step S816, it is determined whether the test evaluation result is sufficient. When the result of the test evaluation is not sufficient (No in step S816), the process returns to step S802. On the other hand, when the result of the test evaluation is sufficient (Yes in step S816), the prediction formula is verified, and the machine learning process ends. As described above, the machine learning process can be sufficiently performed, and the verified prediction formula of the optimum parameter θ can be derived.

  Next, the setting change process in the transport device 100 of this embodiment will be described with reference to FIGS. 1, 2, 6, 7, and 9. FIG. 9 is a flowchart for explaining the setting change process in the transport device 100 of this embodiment.

  For example, the setting change process is started in response to an input of an instruction to convey the sheet S by the conveyance roller 110. First, in step S902, a prediction result is calculated using the prediction formula calculated by the machine learning process. For example, in the image forming apparatus 200, when an image forming instruction is input to the input unit 250, the conveyance of the sheet S is started. For example, before the conveyance of the sheet S is started, the prediction unit 130 predicts the measurement result and the conveyance result of the conveyance roller 110 according to the setting of the conveyance parameter using the prediction formula.

  The prediction formula based on the machine learning process is obtained as described above with reference to FIG. The prediction formula may be obtained after an instruction to convey the sheet S is input. Alternatively, the prediction formula may be obtained prior to the input of the instruction to convey the sheet S.

  In step S904, it is determined whether the prediction result has changed abruptly. For example, it is determined whether the difference between the prediction result and the previous prediction result is larger than the threshold value. When the prediction result does not change rapidly (No in step S904), the process proceeds to step S906. When the prediction result changes abruptly (Yes in step S904), the process proceeds to step S908. If the change in the predicted value is larger than the threshold value with respect to the increase in the number of conveyances, it is determined that the predicted result has changed rapidly.

  In step S906, it is determined whether or not the prediction result deviates from the transport count. For example, it is determined whether or not the difference between the prediction result and the assumed value according to the number of conveyances is larger than the threshold value. If the prediction result deviates from the number of conveyances (Yes in step S906), the process proceeds to step S908. When the prediction result does not deviate from the assumed value (No in step S906), the setting change process ends without changing the setting of the transfer parameter of the transfer device 100.

  In step S908, the user is warned. For example, the display unit 240 displays a screen that prompts a change necessary to extend the life of the transport roller 110. In one example, the display unit 240 displays information that prompts a change in the temperature inside the image forming apparatus 200. Alternatively, the display unit 240 displays information prompting the change of the type of the sheet S. Then, a process progresses to step S910.

  In step S910, the control unit 120 determines whether or not the setting of the transport device 100 has been changed by the user. For example, it is determined whether or not an input related to the change of the setting of the transfer parameter of the transfer device 100 is input to the input unit 250.

  When the setting of the transport device 100 is changed by the user (Yes in step S910), the process proceeds to step S912. In step S912, the control unit 120 changes the setting of the transfer device 100 and the setting of the transfer parameter according to the input from the user. For example, it is set that the temperature of the image forming apparatus 200 and / or the type of the sheet S has been changed.

  If the setting of the transport device 100 has not been changed (No in step S910), the process proceeds to step S914. In step S914, it is determined whether or not the setting of the transport device 100 is automatically permitted to be changed. When it is not permitted to automatically change the setting of the carrier device 100 (No in step S914), the setting change process is terminated without changing the setting of the carrier device 100.

  When it is permitted to automatically change the setting of the transport device 100 (Yes in step S914), the process proceeds to step S916. In step S916, the setting of the transport device 100 is automatically changed. For example, the control unit 120 changes the transport speed of the sheet S based on the prior permission. Alternatively, the control unit 120 controls the heater 112 and / or the fan 144 so as to start driving the heater 112 and / or stop driving the fan 144 based on a prior permission. As described above, the setting of the transport device 100 is automatically changed and the setting change process is ended.

  As described above with reference to FIG. 9, when changing the setting of the conveyance device 100 in the image forming apparatus 200, the setting of the conveyance device 100 is performed after obtaining the user's permission and / or the user's prior permission. Is preferably changed. By changing the setting of the transport device 100, it is possible to postpone the occurrence of a malfunction of the transport device 100 (for example, the end of its life), but the operation of the image forming apparatus 200 may not satisfy the user's request. It is preferable to delay the occurrence of a defect (for example, the end of the life) of the carrier device 100 by changing the setting of the carrier device 100 after obtaining the user's approval.

  In the above description, the transport device 100 is used as a part of the electrophotographic image forming apparatus 200, but the present embodiment is not limited to this. The transport device 100 may be used as a part of the image forming apparatus 200 of another system. For example, the transport device 100 may be used as a part of the inkjet image forming apparatus 200.

  Further, in the above description, the conveyance device 100 conveys the sheet S and is used as a part of the image forming apparatus 200, but the present embodiment is not limited to this. The transport device 100 may be used for purposes other than the image forming apparatus. For example, the conveyance device 100 may convey cloth. In one example, the transport device 100 may be used for printing. Alternatively, the transport device 100 may transport banknotes. In one example, the transport device 100 may be used as a part of an automated teller machine (ATM).

  The embodiments of the present invention have been described above with reference to the drawings. However, the present invention is not limited to the above embodiment, and can be implemented in various modes without departing from the scope of the invention. Further, various inventions can be formed by appropriately combining the plurality of constituent elements disclosed in the above-described embodiments. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, the constituent elements of different embodiments may be combined appropriately. For easy understanding, the drawings mainly show the respective constituent elements, and the thickness, length, number, spacing, etc. of the constituent elements shown in the drawings are not shown for the sake of convenience of drawing. May differ from. Further, the materials, shapes, dimensions, etc. of the respective constituent elements shown in the above-mentioned embodiments are merely examples, and are not particularly limited, and various changes can be made without substantially departing from the effects of the present invention. is there.

  The present invention relates to a carrier device and has industrial applicability.

100 transport device 110 transport roller 120 control unit 130 prediction unit

Claims (8)

A transport roller for transporting the transport target object,
A control unit that controls the transport roller,
Based on the past setting of the transport parameter, the measurement result of the transport roller corresponding to the past setting of the transport parameter or the transport result of the transport object, the transport parameter of the transport parameter for transporting the transport object. A transport device, comprising: a prediction unit that predicts a measurement result of the transport roller or a transport result of the transport target object from a current setting. Further provided with a temperature measurement unit for measuring the temperature,
The transport parameter includes at least a temperature measured by the temperature measuring unit, a transport speed of the transport target, a type of the transport target, and a number of times the transport target transports the transport target. The transport device according to 1.   The transport device according to claim 2, wherein the transport parameter further includes hardness of the transport target and pressure of the transport roller against the transport target.   The transport device according to claim 1, wherein the prediction unit predicts a diameter of the transport roller.   The transport device according to claim 1, wherein the prediction unit predicts at least one of a delay rate and a delay time of the transport target transported by the transport roller.   The transport device according to claim 1, wherein a warning is given to the user based on the prediction result by the prediction unit.   7. The transfer device according to claim 6, wherein the user is warned to change the installation location of the transfer device and the type of the transfer target based on the prediction result by the prediction unit.   The transport apparatus according to claim 1, wherein the control unit changes the current setting of the transport parameter based on a prediction result by the prediction unit.

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