JP6136745B2 - Maintenance judgment device, maintenance judgment system, maintenance judgment program, and maintenance judgment method - Google Patents

Description

  The present invention relates to a maintenance determination device, a maintenance determination system, a maintenance determination program, and a maintenance determination method.

As an image forming apparatus having a function of forming an image on a recording material such as paper, a copying machine, a printer apparatus, a facsimile apparatus, and a multifunction machine having these functions are known.
In such an image forming apparatus, when a failure (including failure or malfunction) that hinders its operation occurs, it causes inconvenience to the user of the image forming apparatus. Therefore, the occurrence of a failure is predicted in the image forming apparatus, and when the occurrence of the failure is predicted, an effective measure for preventing or eliminating the failure (replacement of parts causing the failure, cleaning, adjustment, etc.) ) Is determined (identified), and the result is informed to a maintenance person or the like to perform maintenance work.

Up to now, various inventions have been proposed for techniques related to maintenance of image forming apparatuses and facilities.
For example, Patent Literature 1 acquires a plurality of types of information related to the state of the image forming apparatus, calculates an index value from the acquired plurality of types of information, and based on time-change data of the calculated index value. An invention for determining a subsequent change in the state of the image forming apparatus is disclosed.

  For example, in Patent Document 2, an index value indicating the state of a device is created from a multidimensional signal obtained by observation, and this is compared with a predetermined threshold value to detect a failure of a device that may occur in the near future. Occurrence is predicted, and based on the failure occurrence prediction, an abnormal image type whose expression is predicted when the index value exceeds a second threshold that is a predetermined level is specified, and the abnormal image type is identified from the specified abnormal image type. An invention is disclosed in which an image is artificially generated and printed out, and a user views the output abnormal image and determines whether to continue using the apparatus or replace the apparatus.

  For example, in Patent Document 3, the management apparatus determines an abnormal sign for each of a plurality of image forming units based on a plurality of received state data, and indicates an indication of an abnormal sign for each of the plurality of image forming units. An inference unit that calculates an index value, a replacement information acquisition unit that acquires replacement part information including the replacement date of the image forming unit from the maintenance management system when a diagnosis request is received from the maintenance staff's terminal, and the acquired replacement A determination table creation unit that calculates weight information for the sign determination index value based on the component information; a comprehensive diagnosis information creation unit that calculates comprehensive diagnosis value information for each image forming unit from the sign determination index value and the weight information; An invention is disclosed that includes a comprehensive diagnosis information communication unit that transmits comprehensive diagnosis value information to a maintenance worker's terminal.

JP 2005-017874 A JP 2008-102470 A JP2012-073923A

In recent years, a predictive probability that a failure will occur in the near future of the image forming device is calculated based on information collected from a monitored device (for example, an image forming device), and the image formation is performed according to the calculated predictive probability of the failure. It is considered to instruct maintenance personnel to perform maintenance work on the device.
An object of this invention is to propose the technique which optimizes the timing which instruct | indicates implementation of the maintenance work according to the predictive probability of the failure of a to-be-monitored apparatus.

The present invention (1) is based on information that indicates a point in time when a non-periodic maintenance work is performed on the monitored device, a predictive probability of failure at that point, and information stored in the storage unit. Identify the failure predictor probability when the cumulative number of occurrences of non-scheduled maintenance work that increases with an increase in failure predictor probability exceeds a predetermined reference value, and perform maintenance work on the monitored device Setting means for setting the predictive probability of the specified failure as a threshold value that is a criterion for determining whether or not to perform the failure, a calculation means for calculating the predictive probability of failure for the monitored device, and a failure calculated by the calculation means And a determination unit that determines whether or not the monitored device needs to be subjected to maintenance work based on the predictive probability and the threshold set by the setting unit.

According to the present invention (2), in the present invention (1), the storage means includes information indicating a point in time when the monitored device is subjected to non-periodic maintenance work, and a predictive probability of a failure at the time point. in association with the information identifying the calculating means, for each monitored device, identify and sets the threshold for sign probability of failure when the accumulated value exceeds the reference value, that This is a featured maintenance determination device.

According to the present invention (3), in the present invention (1), when a plurality of monitored devices are classified according to their usage characteristics, the storage means performs a non-periodic maintenance operation on the monitored devices. And the predictive probability of failure at the time point are stored in association with information for identifying the classification to which the monitored device belongs, and the calculation means calculates the cumulative value for each category of the monitored device. identify the sign probability of failure when exceeding a reference value for setting the threshold value, it is the maintenance determination apparatus according to claim.

According to the present invention (4), in the present invention (1) to (3), the storage means includes information indicating a point in time when the non-periodic maintenance work is performed on the monitored device, and a predictive probability of a failure at the point in time. , in association with the information identifying the type of fault occurring in the monitored device, the calculating means, for each type of failure, for the sign probability of failure when the accumulated value exceeds the reference value identify and set the threshold value, it is the maintenance determination apparatus according to claim.

The present invention (5) includes a monitored device to be subjected to maintenance work, and a maintenance determination device that determines the necessity of performing maintenance work on the monitored device. The maintenance determination device is non-periodic with respect to the monitored device. Based on the storage means for storing the information indicating the time point when the maintenance work is performed and the predictive probability of the failure at the time point, and the information stored in the storage means, it increases as the predictive probability of the failure increases. As a threshold value that determines the predictive probability of failure when the cumulative value of the number of occurrences of non-scheduled maintenance work exceeds a predetermined reference value, and serves as a criterion for determining whether maintenance work needs to be performed on the monitored device setting means for setting a sign probability of failure that the specific, calculation means for calculating a sign probability of failure for the monitored device, and the set calculating means presage probability of failure calculated by by the setting unit threshold On the basis of the A maintenance judgment system characterized by comprising judging means for judging whether it is necessary to perform maintenance operations on the monitored device.

The present invention (6) is stored in the computer by a storage function for storing information indicating a point in time when the non-periodic maintenance work is performed on the monitored device and a predictive probability of failure at the time, and the storage function. Based on the information, the failure predictor probability is specified when the cumulative value of the number of occurrences of non-scheduled maintenance work that increases with an increase in the failure predictor probability exceeds a predetermined reference value. A setting function for setting the predictive probability of the identified failure as a threshold value as a criterion for determining whether or not maintenance work needs to be performed, a calculation function for calculating a predictive probability of failure for the monitored device, and a calculation function A maintenance determination program for realizing a determination function for determining the necessity of performing maintenance work on the monitored device based on the predictive probability of the failure and the threshold set by the setting function It is a non.

According to the present invention (7), the maintenance determination device stores information indicating a point in time when the non-periodic maintenance work is performed on the monitored device and a predictive probability of a failure at the point in time, and the stored information is stored in the stored information. Based on this, identify the predictive probability of failure when the cumulative value of the number of occurrences of non-scheduled maintenance work that increases as the predictive probability of failure exceeds a predetermined reference value, and maintain the monitored device The predictive probability of the specified failure is set as a threshold value that is a criterion for determining whether or not work needs to be performed, the predictive probability of failure is calculated for the monitored device, and the calculated predictive probability of failure and the setting unit A maintenance determination method characterized by determining the necessity of performing maintenance work on a monitored apparatus based on a set threshold value.

  According to the present invention (1), (5) to (7), the timing for instructing the execution of the maintenance work according to the predictive probability of the failure of the monitored apparatus is optimized as compared with the case where the present invention is not applied. be able to.

  According to the present invention (2) and (3), the timing for instructing the execution of the maintenance work can be optimized according to the characteristics of the user of the monitored apparatus.

  According to the present invention (4), the timing for instructing the execution of the maintenance work can be optimized according to the characteristics of the failure occurring in the monitored apparatus.

It is a figure which shows the structural example of the maintenance determination system which concerns on one Embodiment of this invention. It is a figure which shows the example of the functional block of a time series data change point detection part and a precursor probability calculation process part. It is a figure which shows the example of the maintenance data which concern on a maintenance operation | work. It is a figure which shows the example of predictive probability data. It is a figure which shows the example of the histogram which shows distribution of the occurrence frequency of the irregular maintenance operation | work with respect to the failure probability. It is a figure which shows the example of the processing flow which concerns on histogram generation. It is a figure which shows the example of the processing flow which concerns on the setting of a threshold value. It is a figure which shows the example of a histogram which changes with the characteristics of a customer. It is a figure which shows the example which normalized the cumulative value of the frequency | count of non-periodic maintenance work which concerns on a histogram. It is a figure which shows the example of the threshold value set for every kind of failure. It is a figure which shows the other example of the threshold value set for every kind of failure.

An embodiment of the present invention will be described with reference to the drawings.
FIG. 1 shows a configuration example of a maintenance determination system according to an embodiment of the present invention.
The maintenance determination system of this example includes an image forming apparatus 100 that forms and outputs an image on a recording material such as paper, and a maintenance data input terminal 200 that is used by an administrator of the image forming apparatus 100 or a person in charge of maintenance work. And have. In the example of FIG. 1, two image forming apparatuses 100 and two maintenance data input terminals 200 are shown, but the number of these is arbitrary.

  In addition, the maintenance determination system of the present example includes a maintenance determination apparatus 300 that is connected to the image forming apparatus 100 and the maintenance data input terminal 200 so that they can communicate with each other by wire or wirelessly. The maintenance determination device 300 determines whether or not it is necessary to perform irregular maintenance work on the image forming device 100 using information collected from the image forming device 100 and the maintenance data input terminal 200. In FIG. 1, the maintenance determination device 300 is configured by one device, but may be configured to be distributed among a plurality of devices.

  The image forming apparatus 100 is an apparatus having a function of forming an image on a recording material such as paper. Examples of the image forming apparatus 100 include apparatuses such as a copying machine, a printer apparatus, and a facsimile apparatus, and also include a multi-function apparatus having a composite function of these apparatuses.

  The image forming apparatus 100 has a function of detecting various image forming parameter values related to control of the image forming process. The image forming parameters include, for example, a photoreceptor potential, a photoreceptor charging current, a semiconductor laser light amount, a developing machine toner density, a primary transfer current, a secondary transfer current, a fixing machine heat roll temperature, and a procompact density (for process control). Patch density). The image forming parameter is a parameter indicating the internal state of the image forming apparatus 100, and can also contribute to the prediction of the occurrence of a failure.

Detection of the value of the image forming parameter is performed at a predetermined timing. For example, for each print output of one page, for each print job in which one or a plurality of pages of print output are collected, for example, for a set time interval (for example, 5 This is done at the timing of every minute). As a value to be detected, a measured value measured at a part corresponding to the image formation parameter may be used, a target value for controlling each part may be used, or a difference between the measured value and the target value may be used. The value of the type may be used.
Further, examples of the parameters indicating the internal state of the image forming apparatus 100 (parameters that can contribute to the prediction of the occurrence of a failure) include environmental parameters indicating the use environment such as the temperature and humidity inside the apparatus. The image forming apparatus 100 of the present example also has a function of detecting these environmental parameters. Hereinafter, image formation parameters and environmental parameters are collectively referred to as monitoring parameters.

  Further, the image forming apparatus 100 transmits the detected monitoring parameter value as detection data to the maintenance determination apparatus 300 together with information for identifying the image forming apparatus 100 and information on the date and time when the monitoring parameter value is detected. The transmission of the detection data to the maintenance determination apparatus 300 may be performed autonomously by the image forming apparatus 100 or may be performed in response to a request from the maintenance determination apparatus 300.

  The maintenance data input terminal 200 performs maintenance work performed by a person who visits the place where the image forming apparatus 100 is installed at the request of the user and actually performs an irregular maintenance work or a person who receives the report. Accepts maintenance data input. The maintenance data to be input includes, for example, information on the date and time when the maintenance work was performed, information for identifying the image forming apparatus 100 subjected to the maintenance work, information indicating the type of failure dealt with in the maintenance work, and maintenance work There is information to show the contents of.

  Here, the maintenance work includes a periodic maintenance work performed at a scheduled date and time, and a non-periodic maintenance work performed at another date and time (for example, when a failure occurs in the image forming apparatus 100, the user In this example, it is assumed that at least maintenance data for irregular maintenance work is input, and in the following, the maintenance data for irregular maintenance work is used. Processing will be performed.

The maintenance data input terminal 200 transmits the input maintenance data to the maintenance determination device 300. The maintenance data transmission to the maintenance determination device 300 may be performed autonomously by the maintenance data input terminal 200 or may be performed in response to a request from the maintenance determination device 300.
In addition to a mode in which maintenance data manually input to the maintenance data input terminal 200 is transmitted, for example, the date of maintenance work and the content of maintenance work (part replacement, adjustment, etc.) are detected on the image forming apparatus 100 side. A function for generating maintenance data may be provided and transmitted to the maintenance determination device 300.

  The maintenance determination device 300 includes a data acquisition unit 301, a monitoring parameter time series data storage unit 302, a time series data change point detection unit 303, a predictive probability calculation processing unit 304, a maintenance data / predictive probability storage unit 305, A histogram generation unit 306, a threshold setting unit 307, a visit determination unit 308, and an output unit 309 are included.

The data acquisition unit 301 receives (acquires) detection data (a monitoring parameter value, information for identifying the image forming apparatus 100, information on a date and time when the monitoring parameter value is detected) from the image forming apparatus 100, and The data is output to the monitoring parameter time series data storage unit 302.
In addition, the data acquisition unit 301 receives maintenance data related to irregular maintenance work (information on the date and time when the maintenance work was performed, information for identifying the image forming apparatus 100 subjected to the maintenance work, from the maintenance data input terminal 200). (Information indicating the type of failure addressed in the maintenance work, information indicating the content of the maintenance work, etc.) is received (acquired) and output to the maintenance data / predictive probability accumulation unit 302.

  The monitoring parameter time-series data storage unit 302 converts the value of the monitoring parameter into a feature amount representing a predetermined section-unit data characteristic based on the detection data acquired from the image forming apparatus 100 by the data acquisition unit 301. The converted feature quantity for each section is stored (accumulated) as time series data of monitoring parameters in association with information for identifying the image forming apparatus 100 and date / time information indicating the section.

  The conversion to the feature amount is performed for each image forming apparatus 100 and each monitoring parameter. As the conversion section, for example, a section expressed in various modes such as a length of time, the number of print jobs, and the number of print pages can be used. In this example, the average value of the monitoring parameter values in the daily unit interval is calculated as the feature value representing the data characteristics. However, depending on the type of failure to be processed, the type of monitoring parameter, etc. Other index values such as a value, a mode value, a standard deviation, a skewness, and a kurtosis may be calculated.

  The time-series data change point detection unit 303 detects a change point at which the time-series transition tendency of the monitoring parameter changes based on the monitoring parameter time-series data stored in the monitoring parameter time-series data storage unit 302. . The time-series data change point detection unit 303 of this example has two or more change point detection algorithms, and detects change points for each change point detection algorithm and each monitoring parameter.

  As the change point detection algorithm, for example, a change point detection algorithm based on a data trend, a change point detection algorithm based on variance (standard deviation value), a change point detection algorithm based on a relationship (correlation coefficient index) of time series data, and a Mahalanobis distance Various algorithms such as a change point detection algorithm can be used.

  The predictive probability calculation processing unit 304 is based on the change point detection algorithm detected by the time-series data change point detection unit 303 and the change point for each monitoring parameter, and the predictive probability that a failure will occur near the image forming apparatus 100 in the near future. Is calculated.

  The time-series data change point detection unit 303 and the sign probability calculation processing unit 304 will be further described. FIG. 2 shows an example of functional blocks of the time-series data change point detection unit 303 and the predictor probability calculation processing unit 304.

  Based on the monitoring parameter group (time series data of each monitoring parameter) 321 stored in the monitoring parameter time-series data storage unit 302, the time-series data change point detection unit 303 includes a plurality of (in this example, Change points are detected using n) change point detection algorithms 323 of # 1 to #n. As a monitoring parameter to be a change point detection target, different parameters may be used according to the characteristics of each change point detection algorithm 323. In addition, a parameter-by-parameter weighting unit 322 that assigns a weighting value (coefficient) for each monitoring parameter is provided in the preceding stage of each change point detection algorithm 323, and the result of multiplying the time-series data of each monitoring parameter by the weighting value Is transferred to the change point detection algorithm 323, and the sensitivity of each monitoring parameter is adjusted for each change point detection algorithm 323.

  The predictive probability calculation processing unit 304 includes a predictive diagnosis model holding unit 325 that holds a predictive diagnosis model in which a method and conditions for calculating a predictive probability that a failure will occur in the near future of the image forming apparatus 100, and each change check. Based on the change point detected for each change point detection algorithm and each monitoring parameter detected by the output algorithm 323 and the predictive diagnosis model held in the predictive diagnosis model holding unit 325, a sign of failure is detected for the image forming apparatus 100 to be processed. A predictive probability calculation unit 324 that calculates the probability. The predictive diagnosis model is prepared for each type of failure, and the predictive probability calculation unit 324 calculates the predictive probability of the failure using the corresponding predictive diagnosis model for each image forming apparatus 100 and for each type of failure.

The failure predictor probability calculated by the predictor probability calculation processing unit 304 is transmitted to, for example, the target image forming apparatus 100 through the output unit 309, and is displayed and output by the operation panel of the image forming apparatus 100 or the like. Further, the failure predictor probability may be displayed and output by another device such as the maintenance data input terminal 200 or the maintenance determination device 300. Note that the output unit 309 may output the predictive probability of the failure by other modes such as voice output and print output instead of such display output.
Furthermore, the failure predictor probability calculated by the predictor probability calculation processing unit 304 includes information for identifying the image forming apparatus 100 to be calculated, information on the date and time when the failure predictor probability is calculated, and the type of failure related to the failure predictor probability. Is output to the maintenance data / predictive probability accumulation unit 305 as predictive probability data.

  The maintenance data / predictive probability accumulation unit 305 includes maintenance data related to non-periodic maintenance work acquired from the maintenance data input terminal 200 by the data acquisition unit 301 and a failure predictive probability calculated by the predictive probability calculation processing unit 304. Store (accumulate) predictive probability data.

  FIG. 3 shows an example of maintenance data related to non-periodic maintenance work stored in the maintenance data / predictive probability storage unit 305. In the example of FIG. 3, “visit date / time” is information on the date and time when the maintenance work was performed, “required time” is information indicating the time required for the maintenance work, and “machine number” is the object of the maintenance work. “Trouble classification” and “Trouble content” are information indicating the type of failure dealt with in the maintenance work, and “Action content”, “Adjustment”, and “Replacement part”. Is information indicating the contents of maintenance work.

FIG. 4 shows an example of predictor probability data stored in the maintenance data / predictor probability storage unit 305. In the example of FIG. 4, the “machine number” is information for identifying the image forming apparatus 100 that is the target for calculating the failure predictor probability, and “date” is the information on the date and time when the failure predictor probability is calculated. “Target trouble” is information indicating the type of failure related to the predictive probability of failure, and “predictive probability” is information indicating the predictive probability of failure.
In this example, the predictive probability calculation processing unit 304 calculates the predictive probability of failure every day, and the predictive probability of daily failure is sequentially recorded in the maintenance data / predictive probability accumulation unit 305.

For each image forming apparatus 100 and each type of failure, the histogram generation unit 306 is based on maintenance data related to irregular maintenance work stored in the maintenance data / predictive probability storage unit 305 and daily predictive probability data. A histogram indicating the distribution of the number of occurrences of non-scheduled maintenance work with respect to the predictive probability of failure is generated.
FIG. 5 shows an example of a histogram showing the distribution of the number of occurrences of non-scheduled maintenance work with respect to the failure predictor probability. In the graph of the figure, the horizontal axis represents the failure predictor probability, and the vertical axis represents the number of occurrences of irregular maintenance work corresponding to the failure predictor probability. In other words, this histogram shows the occurrence of non-scheduled maintenance work for each predictive failure probability based on the predictive probability of failure on the day of non-scheduled maintenance work (the day when the user requested maintenance work). The number of times is shown, and by looking at the histogram, the relationship between the predictive probability of failure and the frequency of occurrence of irregular maintenance work can be grasped.

The threshold value setting unit 307 is a threshold value that serves as a criterion for determining whether or not the image forming apparatus 100 needs to be maintained based on the histogram generated by the histogram generating unit 306 for each image forming apparatus 100 and each type of failure. Set.
The threshold setting by the threshold setting unit 307 is performed after the number of non-periodic maintenance operations to the extent that the tendency of the relationship between the predictive probability of failure and the frequency of occurrence of non-periodic maintenance operations appears in the histogram is performed.

The visit determination unit 308 determines the image forming apparatus 100 based on the failure predictor probability calculated by the predictor probability calculation processing unit 304 and the threshold value set by the threshold setting unit 307 for each image forming apparatus 100 and each failure type. It is determined whether or not maintenance work needs to be performed on 100. That is, when the predictive probability of failure calculated by the predictive probability calculation processing unit 304 is larger than (or more than) the threshold set by the threshold setting unit 307, it is necessary to perform maintenance work on the image forming apparatus 100. It is determined that there is.
The determination by the visit determination unit 308 is performed after threshold values are set by the threshold setting unit 307 for the corresponding image forming apparatus 100 and the type of failure.

  The output unit 309 outputs a maintenance work execution instruction to the image forming apparatus 100 when the visit determination unit 308 determines that it is necessary to perform maintenance work on the image forming apparatus 100. In this example, maintenance work execution instruction information is transmitted to the maintenance data input terminal 200 handled by a maintenance person in charge of the maintenance work of the target image forming apparatus 100, and is displayed and output. It may be performed, and it suffices if a maintenance work instruction is transmitted to the maintenance staff. Further, the output unit 309 may output a maintenance work execution instruction by other modes such as voice output and print output instead of such display output.

The histogram generation unit 306 and the threshold setting unit 307 will be further described.
FIG. 6 shows an example of a processing flow relating to histogram generation by the histogram generation unit 306.
First, the type of failure that is the target of histogram generation and the setting of the target image forming apparatus 100 are set (steps S11 and S12). It also has a counter for the number of occurrences of non-scheduled maintenance work for each section obtained by dividing the failure predictor probability range (0% to 100%) by a certain width unit, and initializes each counter to zero. . In this example, a counter for the number of occurrences of irregular maintenance work is provided for each section divided in 5% increments.

Next, the maintenance data / predictive probability accumulation unit 305 is searched for maintenance data for the target failure type and the target image forming apparatus 100 (step S13), and the date on which the irregular maintenance work was performed is acquired. (Step S14).
Next, from the maintenance data / predictive probability accumulation unit 305, the predictive probability data of the date acquired in step S14 (the date on which non-regular maintenance work was performed) is searched for the target failure type and the target image forming apparatus 100. (Step S15), the failure probability at the time of the date is acquired (Step S16).

  For example, in the example of FIGS. 3 and 4, “machine number” = “123456” is set for the target image forming apparatus 100, and “trouble classification / trouble content” = “image quality trouble / density unevenness” is set for the target failure type. "2013/3/7" is acquired as the date when the non-periodic maintenance work is performed, and "90%" is acquired as the predictive probability of the failure at the date.

Next, 1 is added to the counter of the number of occurrences of non-scheduled maintenance work in the section corresponding to the predictive probability of failure acquired in step S16 (step S17).
Next, it is determined whether or not processing has been completed for all cases of irregular maintenance work (step S18). If it is determined that the processing has not been completed for all cases of non-periodic maintenance work, the processing from step S13 onward is repeated for that case. On the other hand, if it is determined that the processing has been completed for all the cases of non-periodic maintenance work, a histogram is generated by storing the number of occurrences of non-periodic maintenance work for each section in order of the section and stored (step S19).

  By repeating the above processing while switching the target failure type and the target image forming apparatus 100, the number of occurrences of non-periodic maintenance work for the predictive probability of the failure is determined for each image forming apparatus 100 and each failure type. A histogram showing the distribution can be generated.

FIG. 7 shows an example of a processing flow relating to threshold setting by the threshold setting unit 307.
First, for the histogram generated by the histogram generation unit 306, the cumulative value of the number of occurrences of non-periodic maintenance work for each section is calculated in order of the section (step S31), and the cumulative value is normalized (also referred to as normalization). A cumulative frequency value is calculated (step S32).
Next, a section in which the cumulative frequency value exceeds a predetermined reference value (for example, 50%) is specified (step S33), a representative value is determined for the range of failure probabilities for the section, and set as a threshold value. (Step S34). In this example, as the representative value set as the threshold value, the median value of the failure predictor probability range related to the section is used, but other representative values such as an upper limit value and a lower limit value of the range may be used.

  FIG. 8 shows an example of a histogram that varies depending on the characteristics of the customer (user of the image forming apparatus 100). In the graph of the figure, the horizontal axis represents the failure predictor probability, and the vertical axis represents the number of occurrences of irregular maintenance work corresponding to the failure predictor probability. That is, similar to FIG. 5, based on the predictive probability of failure on the day of non-scheduled maintenance work (the day when the user requested maintenance work), non-scheduled maintenance work for each predictive probability of failure It is possible to grasp the relationship between the predictive probability of failure and the frequency of occurrence of irregular maintenance work by viewing the histogram.

The histogram in FIG. 8A is an example of a histogram obtained for a customer with a high image quality requirement level, for example, and the occurrence of irregular maintenance work peaks at a stage where the predictive probability is as low as 40%. . The histogram in FIG. 8B is an example of a histogram obtained for a customer with a low image quality requirement level, for example, and the occurrence of non-periodic maintenance work peaks when the predictive probability is as high as 80%. . The histogram in FIG. 8C is an example of a histogram obtained for a customer who does not output much color information, for example, and the occurrence of irregular maintenance work peaks at a stage where the predictive probability is as high as 80% to 100%. It has become.
Thus, it can be seen that the tendency of the predictive probability of failure when the maintenance request is requested by the user differs according to the characteristics of the customer.

  FIG. 9 shows an example of the cumulative frequency value obtained by normalizing the cumulative value of the number of occurrences of non-periodic maintenance work related to the histogram. In the graph of the figure, the horizontal axis represents the failure predictor probability, and the vertical axis represents the cumulative frequency value corresponding to the failure predictor probability. In the example of FIG. 8, the transition of the cumulative frequency value with an increase in the predictive probability of failure is shown for three histograms having different characteristics. Here, the cumulative frequency value represents the percentage of the cumulative value with respect to the total number of occurrences of non-periodic maintenance work as a percentage.

The threshold value setting unit 307 specifies the predictive probability of failure when the cumulative frequency value transition exceeds the reference value (50% in this example) for each histogram (that is, each customer), and sets the threshold value as a threshold value. . As a result, a threshold value can be set in accordance with customer characteristics. In other words, a low threshold is set for customers who have requested maintenance work at a stage where the predictive probability of failure is low, and a high threshold is set for customers who have requested maintenance work after the predictive probability of failure has increased. Is set.
Therefore, it is possible to determine whether or not there is a need to perform maintenance work using a threshold value set according to customer characteristics, and it is possible to optimize the timing of maintenance work instruction according to customer characteristics.

  Here, in the above description, the reference value related to the cumulative frequency value at the time of setting the threshold is 50%, but other values may be used as the reference value. For example, if the reference value is set low, requests for non-scheduled maintenance work from customers can be reduced and improvement in customer satisfaction can be expected, but voluntary maintenance work increases. If the reference value is set high, the voluntary maintenance work is reduced and the cost required for the maintenance work can be expected to be reduced, but the demand for maintenance work from the customer increases. What reference value is used may be set in consideration of the balance between customer satisfaction and cost required for maintenance work.

  The threshold value may be set by a method other than the method using the cumulative frequency value and the reference value as described above. That is, for example, the failure predictor probability at the time when the cumulative frequency value suddenly increases (for example, when the change rate of the cumulative frequency value becomes equal to or higher than the reference change rate) is specified, and the specified value (from the specified failure predictor probability) For example, the result obtained by subtracting 5% may be set as the threshold value. Further, for example, the histogram generated by the histogram generation unit 306 may be displayed and displayed to a user (for example, a maintenance manager), and may be set by receiving a threshold value input from the user.

  FIG. 10 shows an example of threshold values set for each type of failure for an image forming apparatus 100. In the example of FIG. 10, threshold values are individually set for image quality problems such as “density unevenness”, “line / streak”, “white / color loss”, “dirt”, and “fogging”. As described above, if it is determined whether or not the maintenance work needs to be performed using the threshold value set according to the type of the failure, the maintenance work instruction timing can be optimized according to the characteristic of the failure. In addition, it is possible to perform maintenance visits by preparing appropriate tools, replacement parts, etc. for the prevention and resolution of failures, and it is possible to shorten the time required for maintenance work compared to the conventional case.

  FIG. 11 shows another example of threshold values set for each type of failure for an image forming apparatus 100. FIG. 11A shows an example of a threshold value for a customer who has a large output of color information and a high demand for color variation. The threshold value of other density is high while the threshold value of “density unevenness” is low. FIG. 11B is an example of a threshold value for a customer who frequently prints in black and white and wants to suppress the frequency of jamming, and has a pattern with a low “fogging” threshold value and a “jamming” threshold value. In this way, by setting the threshold according to the customer characteristics and the fault characteristics, the maintenance work instruction timing can be adjusted more appropriately.

  As described above, in the maintenance determination apparatus 300 of this example, the histogram generation unit 306 is based on the maintenance data related to the non-periodic maintenance work stored in the maintenance data / predictive probability storage unit 305 and the daily predictive probability data. And generating a histogram showing the distribution of the frequency of occurrence of non-scheduled maintenance work with respect to the predictive probability of failure, and the threshold setting unit 307 determines the predictive probability of failure and the occurrence frequency of non-scheduled maintenance work from the histogram. Based on this relationship, a threshold value is set as a criterion for determining whether or not maintenance work is required for the image forming apparatus 100. After the threshold is set, the visit determination unit 308 performs maintenance work on the image forming apparatus 100 based on the failure probability calculated by the prediction probability calculation processing unit 304 and the threshold set by the threshold setting unit 307. The output unit 309 outputs a maintenance work execution instruction to the image forming apparatus 100 when it is determined that the maintenance work needs to be performed.

  In this way, by setting the threshold value in consideration of the predictive probability of failure at each time point of a plurality of non-regular maintenance operations performed in the past, the predictive probability of failure in which many non-regular maintenance operations have occurred is reached. The maintenance staff can be encouraged to perform voluntary maintenance work at the previous stage, and the timing for instructing execution of the maintenance work is optimized.

Further, in this example, since a threshold is set by creating a histogram for each customer (user of the image forming apparatus 100), the maintenance staff performs voluntary maintenance work according to the characteristics of the customer. Can be encouraged.
That is, for example, for customers who have requested maintenance work at a stage where the predictive probability of failure is low, the threshold can be set low to reduce the number of non-periodic maintenance work and increase customer satisfaction. be able to. In addition, for example, for customers who have requested maintenance work after a high probability of predicting a failure, the threshold can be set high to reduce the number of spontaneous maintenance work, which is required for maintenance work. Cost can be reduced.

  When the same customer has a plurality of image forming apparatuses 100, the threshold may be set for each image forming apparatus 100, or the threshold may be set for each customer. When setting a threshold value for each customer, information for identifying the image forming apparatus 100 (in this example, “machine number”) is associated with information for identifying the customer, and this correspondence is used for each customer. The thresholds may be set based on the aggregated histograms.

Also, in this example, a threshold is set by creating a histogram for each type of failure, so that the maintenance staff can be encouraged to perform voluntary maintenance work according to the characteristics of the failure.
That is, for example, it is possible to reduce the number of irregular maintenance operations by setting a low threshold for failures that have been requested by customers at the stage where the predictive probability of failures is low. Can be increased. In addition, for example, by setting a high threshold value for a failure that has been requested by a customer for maintenance work after the failure predictor probability has increased, the number of spontaneous maintenance work can be reduced. Costs required can be reduced.
Furthermore, it is possible to prepare a proper tool or replacement part for preventing or eliminating the failure and perform a maintenance visit, and to shorten the time required for the maintenance work as compared with the conventional case.

Here, in the above description, a threshold is set by creating a histogram for each customer (user of the image forming apparatus 100). However, the plurality of image forming apparatuses 100 are classified according to each usage characteristic (by layer). In addition, a threshold value may be set by creating a histogram for each classification.
The image forming apparatus 100 can be classified by various methods. For example, for each image forming apparatus 100, the number of irregular maintenance operations per number of monthly printings is calculated, and based on the result of discretization, Each image forming apparatus 100 may be classified. In this way, the image forming apparatuses 100 are stratified so that the usages of the image forming apparatus 100 are similar to each other belong to the same classification, and are created for each classification unit and set a threshold value, thereby improving processing efficiency. be able to.

  Further, for example, since it is assumed that the usage characteristics are similar for each type of business of a customer (user of the image forming apparatus 100), each image forming apparatus 100 may be classified by business type. As a result, not only can the efficiency of the process be improved, but also a customer who does not have enough non-regular visits can be instructed for maintenance work at an appropriate timing.

  Here, the maintenance determination apparatus 300 of this example includes a CPU (Central Processing Unit) that performs various arithmetic processes, a RAM (Random Access Memory) that is a work area of the CPU, a ROM (Read Only) that records a basic control program, and the like. Memory) and other main storage devices, HDD (Hard Disk Drive) and other auxiliary storage devices for storing various programs and data, display devices for displaying and outputting various information, and operation buttons used for input operations by the operator It is realized by a computer having hardware resources such as an input / output I / F that is an interface with an input device such as a touch panel and a communication I / F that is an interface for performing wired or wireless communication with other devices. ing.

Then, the program according to the present invention is read from the auxiliary storage device or the like, loaded into the RAM, and executed by the CPU, thereby realizing the function of the maintenance determination device according to the present invention on the computer.
In this example, the function of the storage unit according to the present invention is realized by the maintenance data / predictive probability accumulation unit 305, and the function of the setting unit according to the present invention is realized by the histogram generation unit 306 and the threshold setting unit 307. The function of the calculation means according to the invention is realized by the time-series data change point detection unit 303 and the predictor probability calculation processing unit 304, and the function of the determination means according to the present invention is realized by the visit determination unit 308.

Here, the program according to the present invention is set in the computer of the maintenance determination apparatus 300 by, for example, a format read from an external storage medium such as a CD-ROM storing the program or a format received via a communication network. Is done.
Note that the present invention is not limited to a mode in which each functional unit is realized by a software configuration as in this example, and each functional unit may be realized by a dedicated hardware module.

  Further, in this example, the example of the maintenance determination apparatus 300 including both the histogram generation unit 306, the threshold setting unit 307, and the visit determination unit 308 is shown, but the histogram generation unit 306, the threshold setting unit 307, and the visit determination unit 308 are illustrated. May be provided in different devices.

  In the above description, the image forming apparatus 100 is taken as an example of the monitored apparatus, and the determination of the treatment relating to the monitored apparatus has been described. However, the present invention is effective in determining the necessity of maintenance work. These devices may be monitored devices, and there may be a mechanism in which the maintenance determination device 300 collects information necessary for determination of treatment from the monitored devices.

  The present invention can be used for various systems and devices that determine the necessity of maintenance work for monitored devices, and their programs and methods.

100: Image forming apparatus, 200: Maintenance data input terminal, 300: Maintenance determination apparatus,
301: Data acquisition unit 302: Monitoring parameter time series data storage unit 303: Time series data change point detection unit 304: Predictive probability calculation processing unit 305: Maintenance data / predictive probability storage unit 306: Histogram generation unit 307: Threshold setting unit, 308: Visit determination unit, 309: Output unit, 321: Monitoring parameter group, 322: Parameter weighting unit, 323: Change point detection algorithm, 324: Predictive probability calculation unit, 325: Predictive diagnosis model Holding part

Claims (7)

Storage means for storing information indicating a point in time at which an unscheduled maintenance operation is performed on the monitored device, and a predictive probability of a failure at that point;
Based on the information stored in the storage means, the failure predictor probability when the cumulative value of the number of occurrences of non-scheduled maintenance work that increases as the failure predictor probability increases exceeds a predetermined reference value identify the setting means for setting a sign probability of the specified failure as the threshold to be a criterion of whether or not it is necessary to perform maintenance operations on the monitored device,
A calculation means for calculating a predictive failure probability for the monitored device;
A determination unit that determines whether or not the monitoring target apparatus needs to be maintained based on a failure predictor probability calculated by the calculation unit and a threshold value set by the setting unit;
A maintenance determination apparatus comprising: The storage means stores information indicating a point in time at which an unscheduled maintenance operation is performed on the monitored device, and a predictive probability of a failure at the time point, in association with information for identifying the monitored device,
Said calculation means, for each monitored device, identify and sets the threshold for sign probability of failure when the accumulated value exceeds the reference value,
The maintenance determination apparatus according to claim 1. Multiple monitored devices are classified according to their usage characteristics,
The storage means stores information indicating a point in time when a non-periodic maintenance operation is performed on the monitored device, and a predictive probability of failure at the time point, in association with information identifying a classification to which the monitored device belongs,
Said calculation means, for each classification of the monitored device, identify and sets the threshold for sign probability of failure when the accumulated value exceeds the reference value,
The maintenance determination apparatus according to claim 1. The storage means associates information indicating a point in time when a non-periodic maintenance operation is performed on the monitored device and a predictive probability of the failure at the time point with information for identifying the type of failure that has occurred in the monitored device. Remember,
It said calculation means, for each type of failure, setting the threshold value specified by the the sign probability of failure when the accumulated value exceeds the reference value,
The maintenance determination apparatus according to any one of claims 1 to 3, wherein the maintenance determination apparatus according to any one of claims 1 to 3 is provided. A monitored device to be subjected to maintenance work, and a maintenance determination device for determining the necessity of performing maintenance work on the monitored device,
The maintenance judgment device
Storage means for storing information indicating a point in time at which an unscheduled maintenance operation is performed on the monitored device, and a predictive probability of a failure at that point;
Based on the information stored in the storage means, the failure predictor probability when the cumulative value of the number of occurrences of non-scheduled maintenance work that increases as the failure predictor probability increases exceeds a predetermined reference value identify the setting means for setting a sign probability of the specified failure as the threshold to be a criterion of whether or not it is necessary to perform maintenance operations on the monitored device,
A calculation means for calculating a predictive failure probability for the monitored device;
A determination unit that determines whether or not the monitoring target apparatus needs to be maintained based on a failure predictor probability calculated by the calculation unit and a threshold value set by the setting unit;
A maintenance judgment system characterized by comprising: On the computer,
A storage function for storing information indicating a point in time at which an unscheduled maintenance operation is performed on the monitored device, and a predictive probability of a failure at the point,
Based on the information stored by the storage function, the predictive probability of failure when the cumulative value of the number of occurrences of non-scheduled maintenance work that increases as the predictive probability of failure exceeds a predetermined reference value identify the, a setting function for setting a sign probability of the specified failure as the threshold to be a criterion of whether or not it is necessary to perform maintenance operations on the monitored device,
A calculation function for calculating a predictive probability of failure for the monitored device;
A determination function for determining whether or not there is a need to perform maintenance work on the monitored device based on the predictive probability of failure calculated by the calculation function and the threshold set by the setting function;
Maintenance judgment program for realizing. Maintenance judgment device
Stores information indicating the point in time at which non-periodic maintenance work was performed on the monitored device, and the predictive probability of failure at that point,
Based on the stored information, a failure predictor probability is specified when the cumulative value of the number of occurrences of non-scheduled maintenance work that increases with an increase in failure predictor probability exceeds a predetermined reference value. And setting the predictive probability of the identified failure as a threshold value as a criterion for determining whether or not maintenance work needs to be performed on the monitored device,
Calculate the predictive probability of failure for the monitored device,
Based on the calculated predictive probability of failure and the threshold set by the setting means, it is determined whether or not there is a need to perform maintenance work on the monitored device.
A maintenance judgment method characterized by the above.

Images