JP5045191B2 - Failure prediction diagnosis device, failure prediction diagnosis system using the same, and failure prediction diagnosis program - Google Patents

Description

The present invention relates to a failure prediction diagnosis apparatus, a failure prediction diagnosis system using the same , and a failure prediction diagnosis program .

2. Description of the Related Art Conventionally, various failure prediction diagnosis systems that predict in advance a failure state of a diagnosis target device such as an image forming apparatus have been provided.
In Patent Document 1, a plurality of types of information related to the state of the image forming apparatus is acquired, and the Mahalanobis distance from the normal reference space is calculated using the acquired information, and the distance is calculated based on a predetermined threshold. Is larger, the technology for determining that the possibility of failure is high is disclosed. In this technique, temperature and humidity information is used for calculation of the Mahalanobis distance with the same weight as other information such as vibration, toner density, and charging potential among a plurality of types of information.
Patent Document 2 includes information on abnormality for each copying machine included in the failure information, the level of proficiency of the user with the copying machine, the degree of feeling as a failure, the characteristics of the paper and document used, and the installation of the apparatus. A technique for calculating the severity in consideration of the environment and performing a failure diagnosis based on the severity has already been disclosed. In this technique, temperature and humidity are used to calculate severity along with other information (device information, user proficiency, paper or document characteristics, etc.).
Furthermore, Patent Document 3 discloses a technique that enables estimation of a failure location by fuzzy reasoning using an information amount indicating the use state of the apparatus, and shortens the repair time from the occurrence of the failure. In this technology, the mixing ratio (absolute humidity) is obtained from the temperature and humidity, and is used as one of the information amounts together with the separation difference current output value and the separation difference current adjustment value, and the separation difference current adjustment failure, separation charger failure, The rate of transfer material failure is estimated.

Japanese Patent Laying-Open No. 2005-17874 (Embodiment of the Invention, FIG. 1) JP-A-8-30152 (Example, FIG. 5) JP-A-6-208265 (Example, FIG. 9)

The present invention is intended to provide a failure prediction diagnosis apparatus that accurately predicts a failure state that is likely to depend on environmental conditions, a failure prediction diagnosis system using the same , and a failure prediction diagnosis program .

The invention according to claim 1 diagnoses the failure prediction state of the diagnosis target device that can be stored together with the environmental information including the information about the temperature and the humidity about the internal information including the information about the operation state of the diagnosis target device and the information used for the control. a failure prediction diagnosis apparatus, an information collecting means for collecting said internal information and the environment information from the diagnosis target device, the interior of the diagnosis target equipment failure prediction index corresponding to the failure predicted state of the diagnosis target device calculating possible regression model based on the information, based on the regression model setting means for setting in advance respectively for each environmental compartments separated by a range of temperature and humidity, the environment information more collected in the information collection means, said corresponding to environmental compartments separated by a range of temperature and humidity of the environmental information from a plurality of regression models set in advance by the regression model setting means A regression model selecting means for selecting a regression model that, based on the regression equation as selected regression model in the regression model selecting means, by using the internal information of the diagnosis target device collected by the information collecting means calculating a failure predictor of diagnosis target device is the failure prediction diagnosis apparatus characterized by comprising: a failure prediction determination means for determining a failure prediction state diagnosis target device on the basis of the failure prediction index to which the calculated and .

The invention according to claim 2 is the failure prediction diagnosis apparatus according to claim 1, wherein the failure prediction diagnosis apparatus is an image forming apparatus that forms an image on a recording material conveyed by the diagnosis target device. is there.
The invention according to claim 3 is the failure prediction diagnosis apparatus according to claim 1 or 2, wherein the failure prediction indicator, diagnosed failure risk showing the danger degree leading to equipment failure, the failure emergency indicating an emergency degree for the failure It is a failure prediction diagnosis device characterized by being one of a failure severity indicating a degree of failure and a degree of failure symptom .
The invention according to claim 4 is the failure prediction diagnosis apparatus according to any one of claims 1 to 3, wherein the regression model setting means sets a plurality of regression models using logistic regression analysis. It is a failure prediction diagnosis device.
The invention according to claim 5 is the failure prediction diagnosis apparatus according to any one of claims 1 to 3, wherein the regression model setting means, as the internal information of the diagnosis target device to be used for setting the regression model, the diagnosis target device A failure prediction and diagnosis apparatus characterized by including internal information at the time of failure and internal information after repair of a failed diagnosis target device.

The invention according to claim 6 is the failure prediction diagnosis apparatus according to any one of claims 1 to 3 , wherein the regression model setting means takes into account use conditions and environmental conditions of the diagnosis target device, and uses a regression equation as a regression model. Is a failure predictive diagnosis device characterized in that it is updated regularly or irregularly .
The invention according to claim 7, it in failure prediction diagnosis apparatus according to any one of claims 1 to 3, wherein the regression model setting means is one that was classified into a plurality in the failure factor regression model is created for each environmental compartments Is a failure prediction and diagnosis apparatus characterized by
The invention according to claim 8, failure in either the failure prediction diagnosis apparatus according to any one of claims 1 to 7, wherein the failure prediction determining means, failure prediction index calculated corresponds to which pre partitioned divided region A failure prediction diagnostic apparatus characterized by determining a prediction state.
The invention according to claim 9 is the failure prediction diagnosis apparatus according to claim 8, wherein the failure prediction determination means variably sets a threshold value for dividing the failure prediction index. Device.
The invention according to claim 10 is the failure prediction diagnosis apparatus according to claim 8 or 9, wherein the failure prediction determination means, dispatch of maintenance specialists under the conditions failure prediction index exceeds a predetermined threshold value highly urgent It is a failure prediction diagnostic device characterized by determining.
The invention according to claim 11 is the failure prediction diagnosis apparatus according to any one of claims 1 to 10, further comprising display means capable of displaying the failure prediction state determined by the failure prediction determination means. It is a failure prediction diagnosis device.

The invention according to claim 12 includes a plurality of diagnostic target devices that can be stored together with environmental information including information about temperature and humidity with respect to internal information including information related to the operating state of the diagnostic target device and information used for control, and each diagnostic target a failure prediction diagnosis system including a failure prediction diagnosis apparatus, the diagnosing faults predicted state of each diagnosis target device are connected via a usable communication networks and equipment, the failure prediction diagnosis apparatus, each an information collecting means for collecting communicatively said internal information and the environment information from the diagnosis target device, a failure prediction index corresponding to the failure predicted state of the diagnosis target device can be calculated on the basis of the internal information of the diagnosis target device the Do regression model, the regression model setting means for setting in advance respectively for each environmental compartments separated by a range of temperature and humidity, more said information collecting means Based on the condenser has been the environmental information, to select a regression model corresponding to separated environment classification in a range of temperature and humidity of the environmental information from a plurality of regression models set in advance by the regression model setting means a regression model selecting means, based on the regression equation as selected regression model in the regression model selection unit, diagnosed equipment failure prediction using the internal information of the diagnosis target device collected by the information collecting means calculating an index, a failure prediction diagnosis system comprising: the failure prediction determining means, the determining a failure prediction state diagnosis target device on the basis of the failure prediction index the calculated.
The invention according to claim 13 is the failure prediction diagnosis system according to claim 12, wherein the information collection means periodically collects internal information from each diagnosis target device together with environmental information. is there.
Invention, the failure prediction diagnostic system according to claim 12, wherein the information collection means, the information collecting internal information of diagnostic object data and the diagnostic target device identifies a diagnosis target device, the environment information is collected according to Claim 14 It is a failure prediction diagnosis system characterized by collecting date and time information together.
The invention according to claim 15 diagnoses the failure prediction state of the diagnosis target device that can be stored together with the environmental information including the information about the temperature and the humidity about the internal information including the information about the operation state of the diagnosis target device and the information used for the control. A computer used in the failure prediction diagnosis apparatus includes information collection means for collecting the internal information and the environment information from the diagnosis target device, and a failure prediction index corresponding to the failure prediction state of the diagnosis target device. Regression model that can be calculated based on the internal information, a regression model setting unit that presets each of the environmental categories divided by temperature and humidity ranges, and based on the environmental information collected by the information collection unit, Among the plurality of regression models set in advance by the regression model setting means, the environmental information is divided within the temperature and humidity ranges. A regression model selection means for selecting a regression model corresponding to the selected environmental category, and a regression equation as a regression model selected by the regression model selection means, based on the regression equation selected by the information collection means. A failure to function as failure prediction determination means for calculating a failure prediction index of the diagnosis target device using the internal information and determining a failure prediction state of the diagnosis target device based on the calculated failure prediction index It is a predictive diagnostic program.

According to the first aspect of the present invention, it is possible to accurately predict a failure state that tends to depend on environmental conditions for the diagnosis target device.
According to the second aspect of the present invention, it is possible to accurately predict a failure state that tends to depend on environmental conditions for an image forming apparatus as a diagnosis target device.
According to the third aspect of the present invention, it is possible to accurately predict any one of failure risk, failure urgency and failure severity as a failure prediction index of the diagnosis target device.
According to the invention which concerns on Claim 4, an exact regression model can be set easily.
According to the invention which concerns on Claim 5, a regression model can be easily constructed | assembled using the past failure example.
According to the invention which concerns on Claim 6, the influence of a season can be reflected effectively and a failure state can be estimated more correctly.
According to the invention which concerns on Claim 7, a failure state can be estimated more correctly for every failure factor.

According to the invention which concerns on Claim 8, a failure state can be correctly estimated based on the value of a failure prediction parameter | index.
According to the ninth aspect of the present invention, it is possible to finely adjust the judgment criterion of the failure prediction state based on the failure prediction index.
According to the invention which concerns on Claim 10, before a failure generate | occur | produces in a diagnostic object apparatus, a maintenance inspection worker can be dispatched and a maintenance inspection work can be performed in advance.
According to the invention which concerns on Claim 11, the prediction result of a failure state can be grasped | ascertained correctly.
According to the twelfth aspect of the present invention, it is possible to accurately predict a failure state that tends to depend on environmental conditions for a plurality of devices to be diagnosed.
According to the thirteenth aspect of the present invention, it is possible to always monitor the states of a plurality of diagnosis target devices and accurately predict a failure state.
According to the fourteenth aspect of the present invention, it is possible to specify a diagnosis target device and accurately predict a failure state while observing changes over time.
According to the fifteenth aspect of the present invention, it is possible to easily construct a failure prediction diagnosis apparatus that can accurately predict a failure state that is likely to depend on environmental conditions for a diagnosis target device.

First, an outline of an embodiment model to which the present invention is applied will be described.
Outline of Embodiment Model FIG. 1 shows an outline of an embodiment of a failure prediction diagnosis system to which the present invention is applied.
In the figure, the failure prediction diagnosis system includes a plurality of diagnosis target devices 10 (for example, information that can be stored together with environmental information including information about temperature and humidity with respect to internal information including information related to the operation state of the diagnosis target device and information used for control). 10 (1), 10 (2), 10 (3)... 10 (n)), and a failure prediction state of each diagnosis target device 10 connected via the communication network 11 capable of communicating with each diagnosis target device 10. a failure prediction diagnosis systems with a failure prediction diagnosis apparatus 1 for diagnosing the failure prediction diagnosis apparatus 1 collects communicatively said internal information and the environment information from the respective diagnosis target device 10 information a collection unit 2, the diagnosis target the calculated possible regression models based on internal information of the failure the failure predictor corresponding to the predicted condition diagnosis target device 10 of the apparatus 10 M (e.g. Ma to Md), Environmental classification E (e.g. EA to ED) regression model setting means 3 for setting in advance respectively for each separated by a range of degrees and humidity, based on the environment information and more collected in the information collecting unit 2, the regression model a regression model selecting means 4 for selecting a regression model M corresponding to the environmental compartments separated by a range of temperature and humidity of the environmental information from a plurality of preset regression model M by the setting means 3, the regression model based on the regression equation as selected regression model M at the selection unit 4, the failure predictor of diagnosis target device 10 is calculated by using the internal information of the diagnosis target device 10 collected by the information collecting means 2 are those comprising a failure prediction determining means 5 for determining the failure prediction state diagnosis target device 10 on the basis of the failure prediction index the calculated, the.
In this aspect, the failure prediction determination means 5 is a method of calculating a failure prediction index using a regression model M selected in advance according to the environment information, so that the environment information is used as a parameter for creating a regression model. Compared to the mode of direct use, the concern that the environmental information leads to a failure prediction index calculation error is effectively avoided.

In such technical means, the diagnosis target is a failure prediction state of the diagnosis target device 10, and it is possible to predict and prevent a failure of the diagnosis target device 10 in advance.
Here, the 'internal information' may be information relating to one type of failure factor, but from the viewpoint of predicting failure prediction over a wide range, it is preferable to collect information relating to a plurality of types of failure factors.
Moreover, although it is preferable to use temperature and humidity information as the “environment information”, it is possible to use only temperature or humidity information.
Further, as the information collecting means 2, when used in a failure prediction diagnosis system, a communication line with a communication network 11 connection is usually used, but the information collecting means 2 is not limited to this and is collected via a recording medium or the like. You may make it do.

Furthermore, as the regression model setting means 3, it is sufficient that a plurality of regression models M capable of calculating a failure prediction index are set for each environment category E. Usually, internal information on past failure cases is used or the same model is used. If there is no past case, the internal information on the failure case of the related model may be used.
In the present embodiment model, the “failure prediction index” may be appropriately selected as long as it is an index indicating the degree of the failure prediction state.
Further, the regression model selection means 4 may be any one that selects the regression model M set for each environment category E based on the environment information of the diagnosis target device 10, for example, a plurality of regression models for each environment category E. In the aspect in which the model M is prepared, at least one may be selected.
Furthermore, the failure prediction determination means 5 may be any device that determines failure prediction based on the failure prediction index, and the threshold value and failure prediction countermeasures may be appropriately selected.
Here, the selection time of the regression model M by the regression model selection means 4 may be at least performed before the judgment process by the failure prediction judgment means 5, and the regression model selection means 4 and the failure prediction judgment means 5 are It can be done in parallel.

In the present embodiment model, a typical aspect of the diagnosis target device 10 includes an image forming apparatus having high environment dependency. The image forming apparatus here may be an image forming method or the presence or absence of a post-processing apparatus as long as it forms an image on a sheet to be conveyed.
Furthermore, as the “failure prediction index”, for example, a failure risk indicating the degree of risk leading to the failure of the diagnosis target device 10, a failure urgency indicating the urgency with respect to the failure of the diagnosis target device 10, and a symptom of the failure of the diagnosis target device 10 However, from the viewpoint of effectively preventing and predicting failure, it is preferable to use failure risk as a failure prediction index.

Further, from the viewpoint of maintaining good information collection efficiency in the information collecting means 2, a method of regularly collecting internal information together with environmental information from each diagnosis target device 10 is preferable.
Furthermore, from the viewpoint of specifying the diagnosis target device 10 and accurately predicting the failure state while observing the change over time, the information collecting unit 2 determines the diagnosis target information and the diagnosis target device 10 for specifying the diagnosis target device 10. It is preferable to collect the internal information and the information collection date and time information for collecting environmental information.

The regression model setting means 3 may set a regression model M using a known regression analysis, but a representative method is to set a plurality of regression models M using, for example, logistic regression analysis. Is mentioned.
Further, from the viewpoint of easily constructing the regression model M in the regression model setting means 3, as the internal information of the diagnostic target device 10 used when setting the regression model M, when the diagnostic target device 10 fails. It is preferable to use information including internal information and internal information after repairing the failed diagnostic target device 10.
Furthermore, from the viewpoint of reflecting the influence of the season more accurately, the regression model setting means 3 updates the regression equation as the regression model M in consideration of the use conditions and environmental conditions of the diagnostic target device 10. It is preferable. In this aspect, the update time may be regular or may be appropriately selected such as a user designated time.
The regression model setting means 3 only needs to have at least a regression model M created for each environment category E. From the viewpoint of more detailed diagnosis prediction, the regression model M for each environment category E is selected. You may make it classify | categorize into multiple according to a failure factor.

As a typical determination method by the failure prediction determination means 5, there is a method in which the failure prediction state is determined based on which region the calculated failure prediction index corresponds to in advance .
In this aspect, from the viewpoint of finely adjusting the judgment criterion of the failure prediction state based on the failure prediction index, it is preferable to variably set a threshold value for classifying the failure prediction index.
Further, when considering the dispatch of a maintenance inspection specialist (service engineer / service man), the failure prediction determination means 5 is performed under the condition that the failure prediction index exceeds a predetermined threshold with high urgency. A mode in which dispatch is determined is preferable.
Furthermore, from the viewpoint of accurately grasping the prediction result of the failure state, it is preferable to include a display unit 6 that can display the failure prediction state determined by the failure prediction determination unit 5.

Hereinafter, the present invention will be described in more detail based on embodiments shown in the accompanying drawings.
Embodiment 1
FIG. 2 shows the overall configuration of the failure prediction diagnosis system according to the first embodiment.
In the figure, the failure prediction diagnosis system can communicate with a plurality of image forming apparatuses 20 (specifically, 20 (1), 20 (2), 20 (3)... 20 (n)), respectively. A maintenance center 22 connected via a communication network 21 is provided.
In the present embodiment, the maintenance center 22 has a failure prediction diagnosis device 40 for diagnosing the failure prediction state of each image forming apparatus 20, and this failure prediction diagnosis apparatus 40 is managed by each image forming apparatus 20. A communication unit 41 for receiving information, a storage unit 42 for storing management information received by the communication unit 41, a regression equation as a regression model necessary for performing failure prediction diagnosis, and various input operations An input processing unit 43, an analysis processing / control unit 44 for controlling analysis processing for a failure prediction diagnosis request from the input unit 43, and a display unit 45 for displaying processing results by the analysis processing / control unit 44. I have.

In the present embodiment, as shown in FIG. 3, the image forming apparatus 20 forms an image on, for example, a sheet as a recording material in an image forming unit 31 such as an electrophotographic system. A storage unit 32 that stores processing programs and the like, a communication unit 33 that enables communication with the failure prediction / diagnosis device 40, and a control unit 34 that controls the image forming unit 31, the storage unit 32, and the communication unit 33 are provided. .
Further, the control unit 34 includes a temperature detector 36 for detecting the temperature inside or around the image forming apparatus 20, a humidity detector 37 for detecting the humidity inside or around the image forming apparatus 20, and a paper conveyance path. Are connected to a position detector 38 used for paper conveyance control and jam processing, and a paper counter 39 for counting the number of paper sheets.
The control unit 34 performs image forming processing by the image forming unit 31 in accordance with an image forming program stored in the storage unit 32 in advance, or management information necessary for operation management of the image forming apparatus 20 with respect to the storage unit 32. Is stored, or management information is transmitted to the failure prediction diagnosis apparatus 40.

As the management information described above, in addition to the internal information of the image forming apparatus 20, environmental information including temperature information from the temperature detector 36 and humidity information from the humidity detector 37, and further, a machine number of the image forming apparatus, And these data collection date information is used.
Here, supplementing the internal information of the image forming apparatus 20, there is a warning (warning) or a fail signal that the image forming apparatus 20 automatically sends depending on the operation state, each set value or observation value used for control, and the like. It is stored in a non-volatile memory as a storage unit 32 in the apparatus. For example, there are four types of fail signals: system failure, local failure, paper jam failure, and document jam failure. There are about 20-30 failures for each type. These are detected by various detectors (for example, the position detector 38) and a program provided in the image forming apparatus 20, and the content and number of failures and the history of failures that occurred in the past are also counted as the paper count when the failure occurs. The number is stored in the image forming apparatus 20 together with the number, and the latest fixed number of failures (for example, 20 for each type) is held.

FIG. 4 shows an example of internal information of the image forming apparatus 20.
In the figure, failure information indicates failure information that occurs in each part of the image forming apparatus 20.
IOT_LogicFail: Failure in the image output unit ESS_FanFail: Fan failure in the transmission subsystem (image signal generator) SoftFail: Failure in the software SensorC_Fail: Sensor failure USB_Open_Fail: Connection failure in the USB cable Communication Fail (for example, communication fail) Communication failure with image reader)
The jam information is jam detection information from the position detector 38 provided in each part of the sheet conveyance path.
Fuser_Jam: Jam at the fixing portion of the image forming unit 31 Regi_Jam: Jam on the alignment roll (registration roll) of the image forming unit 31 FeedOut_Jam: Jam at the paper supply unit Exit_Jam: Jam near the discharge roll TakeAway_Jam: Predetermined transport roll Further, the information on the current counter value−the counter value at the time of occurrence of abnormality is information indicating the number of sheets that have been normally imaged.

Further, in the present embodiment, the analysis processing / control unit 44 of the failure prediction / diagnosis device 40 executes each processing as shown in FIG.
(1) Management information request processing (see steps 51 to 53)
This periodically transmits a management information request signal to the image forming apparatus 20 to be diagnosed, and accordingly, receives management information from the image forming apparatus 20 side, and stores the received management information in the storage unit 42. To be stored.
(2) Regression formula creation (see step 54)
For example, as shown in FIG. 6, the internal information of the image forming apparatus 20 that has been requested for repair due to failure (collected as failure internal information) is collected together with temperature and humidity data. Thereafter, internal information of the image forming apparatus 20 that has been repaired and is operating normally (collected as normal internal information) is collected together with temperature and humidity data.
At this time, it is preferable to extract internal information and temperature / humidity data from a plurality of image forming apparatuses 20.
The temperature / humidity data is distributed, for example, in the range of 20 ° C. to 65 ° C. for temperature data and 124 to 135 (arbitrary relative value) for humidity data.
In the present embodiment, for example, as shown in FIG. 7, the temperature data area is divided into three and the humidity data area is divided into three and divided into nine environmental categories A to I.
In FIG. 7, one point indicates a plurality of image forming apparatuses having the same temperature and humidity. In this example, the central environmental section E includes approximately 50% of the image forming apparatuses.
In FIG. 7, using the failure internal information and normal internal information of the image forming apparatus falling into nine environment categories A to I, regression equations as regression models of nine failure risks for each of the environment categories A to I are obtained. create.
Here, for example, as shown in FIG. 8, a regression equation using logistic regression analysis is employed.
The created regression formula of failure risk is assigned an identification number and stored in the storage unit 42.

As described above, in this embodiment, a method of creating one regression model for each of the environmental classifications A to I may be adopted. However, from the viewpoint of performing a more detailed failure prediction diagnosis, the environmental classifications A to I are used. It is preferable to further categorize each failure factor in relation to each other and create a regression equation of failure risk for each category.
In other words, as described above, the internal information of the image forming apparatus in the past failure cases is used to create the regression formula of the failure risk, but the image forming apparatus that has failed in the past knows the failure location and the failure factor. So, for example, failure factors are divided into categories such as paper feeding troubles (jam troubles), image quality troubles, machine troubles, etc., and regression equations are created using the internal information of the image forming apparatus that caused each trouble. You can do it.

For example, in the case of a paper feed trouble, a regression equation of the failure risk of the paper feed trouble is created. The internal information related to the paper feeding trouble includes a paper jam failure, a document jam failure, a feed number interval at the time of failure, a local failure (failure related to a position detector for detecting the paper position), a consumable limit rate, and the like. Here, the limit rate of consumables means a value obtained by dividing the current use count by the limit use count of a roll, solenoid, motor, or the like used for paper feeding.
Therefore, the regression equation of the failure risk P (corresponding to D in FIG. 8) of the paper feed trouble is
Failure risk P = 1 / (1 + exp (−X))
However, X = A1 × (total number of paper jam failures) + B1 × (total number of document jam failures) + C1 × (average number of feeds between failures) + D1 × (total number of all paper feeds) + E1 × (consumption Product margin rate)
The coefficients A1 to E1 may be determined using internal information of past failure cases.
At this time, in the past failure cases of the image forming apparatus, the internal information before the service engineer performs the maintenance inspection of the paper feed trouble and the internal information after the maintenance inspection (some internal information is Therefore, the coefficient of A1 to E1 (corresponding to K1 to K3 in FIG. 8) is obtained with P = 1 before maintenance inspection and P = 0 after maintenance inspection. This procedure is usually called logistic regression analysis.

Further, the regression equation of the failure risk degree G (corresponding to D in FIG. 8) of the image quality trouble is created using internal information categorized into image quality.
In other words, failure risk G = 1 / (1 + exp (−Y)) for image quality trouble
However, Y = A2 × (total number of system failures) + B2 × (total number of local failures) + C2 × (sensor measurement value related to image quality) + D2 × (average number of feeds between failures) + E2 × (consumables Marginal rate)
The consumable limit rate here is a consumable item related to image quality, and is a value obtained by dividing the current use count by the limit use count of drums, developers, and the like.
Then, the coefficients A2 to E2 (corresponding to K1 to K3 in FIG. 8) may be determined using internal information of past failure cases as in the case of the paper feed trouble.
Specifically, the values of A2 to E2 may be determined by using a logistic regression analysis method from the internal information before and after the date when the service engineer (serviceman) visited in the past due to image quality trouble.
In addition, when there is little data regarding past internal information, calculation may not be possible as described above. Furthermore, since the characteristics of the image forming apparatus differ depending on the model, when there is a new new image forming apparatus introduced, there is no data regarding internal information of past failure cases, so data regarding internal information of other models is used. May be.

(3) Failure prediction diagnosis processing (see steps 55 to 61 in FIG. 5)
This is a process when, for example, a service engineer (serviceman) requests failure prediction diagnosis for an image forming apparatus to be diagnosed.
At this time, the service engineer may input the machine number and data collection date of the image forming apparatus to be diagnosed from the input unit 43, and the analysis processing / control unit 44 stores the data as shown in FIG. The corresponding internal information and environmental information (temperature / humidity data) in the section 42 are extracted, and a regression formula for failure risk is selected based on the environmental information (temperature / humidity data). The internal information extracted together with the information is input, the failure risk based on the environmental information is calculated, and the value is displayed on the screen of the display unit 45 so that it can be referred to.
At this time, a plurality of regression equations of failure risk stored in the storage unit 42 are created for each of the environmental categories A to I, and are stored in the storage unit 42 together with the identification numbers. For this reason, if environmental information is known, it can respond to an identification number and a regression equation can be selected.

Further, the calculated failure risk is stored in the storage unit 42 together with the machine number, data collection date, and identification number of the selected regression equation, and the failure risk is displayed on the display unit 45 together with the machine number and data collection date. .
However, when the failure risk is already calculated from the machine number of the image forming apparatus to be diagnosed and the data collection date and stored in the storage unit 42, the failure risk is read from the storage unit 42 and displayed on the display unit 45. It is possible to display the failure risk along with the machine number and date of data collection.
Here, when the past history of failure risk is displayed in a graph, the machine number and the date range to be displayed may be designated from the input unit 43, and the above calculation is automatically repeated on the display unit 45. A graph corresponding to the past history can be displayed (see, for example, FIG. 12).
In particular, as a regression formula of failure risk, in the case where the category is classified for each failure factor, if the failure risk of the paper feed trouble is greater than the failure risk of the image quality trouble, the paper feed trouble It is possible to determine that the risk of failure is high, and the value of the risk of failure can be calculated. This value can be used as a criterion for determining whether or not a service engineer needs maintenance when internal information is given (see, for example, FIG. 12).

At this time, the failure risk is displayed as a probability value between 0 and 1, and the failure prediction state of the image forming apparatus is indicated by the value.
If it is less than a predetermined threshold (for example, 0.5), it can be determined that the service engineer does not need to visit the user. For example, in the case of 0.5 or more and 0.7 or less, it is possible to watch over the image forming apparatus that is the object of diagnosis because attention is required without visiting the user. If a predetermined threshold value (for example, 0.7) is exceeded, it is determined that the failure of the image forming apparatus is near as shown in FIG. 9, and dispatch of a service engineer (service man) is determined. Even if there is no contact from the user, a service engineer (service man) may visit the user and perform preventive maintenance.
When failure prediction diagnosis is requested for a plurality of image forming apparatuses, the same failure prediction diagnosis processing is performed for other image forming apparatuses.

The storage unit 42 in the failure prediction diagnosis apparatus 40 stores data in which the code number assigned to the service engineer and the machine number are associated with each other, and when the service engineer inputs his / her code from the input unit 43. The list of machine numbers in charge can be displayed on the display unit 45 together with the degree of failure risk, and it is possible to grasp all the image forming apparatuses that need to be prevented from being inspected (for example, see FIG. 12).
Further, the storage unit 42 in the failure prediction diagnosis apparatus 40 stores data in which the area code and the machine number are associated with each other, and the code that can specify the area where the image forming apparatus is installed from the input unit 43. In other words, when the area code is input, a list of machine numbers in the area can be displayed on the display unit 45 together with the failure risk level, and it is possible to grasp all of the image forming apparatuses that require prevention of maintenance and inspection.

(4) Failure Risk Threshold Change Processing As shown in step 62 in FIG. 5, the failure risk threshold value has different characteristics depending on the model of the image forming apparatus, so that the threshold value can be changed after actual operation. It is preferable to do.

(5) Update regression equation (see step 54)
Since it is empirically known that the number of failures occurring in an image forming apparatus also changes depending on use conditions and environmental conditions, the regression equation for calculating the risk of failure needs to be updated periodically. . For example, jam failure is known to increase in the winter due to the relationship of humidity, so re-calculating the regression equation regularly (for example, every month) or irregularly when considering the influence of the season The method is more preferred. At this time, it is possible to change the failure risk threshold to cope with a change in the failure, but it is better to update the regression equation.

Modified Embodiment FIG. 10 shows a modified embodiment of the failure prediction diagnosis system according to the first embodiment.
In the figure, the failure prediction diagnosis system includes a communication network from not only the failure prediction diagnosis device 40 of the maintenance center 22 but also a portable information terminal 70 (for example, a notebook personal computer 70a, a PDA 70b, a mobile phone, etc.) owned by a service engineer. It is possible to collect information on the degree of failure risk via 21.
In this case, the service engineer may input his / her code to access the failure prediction diagnosis apparatus 40 and perform a series of failure prediction diagnosis processes.

FIG. 11 is a bar graph showing the number of times a service engineer (serviceman) visits a customer due to a paper feed trouble such as a jam in the image forming apparatus.
In the figure, there is a gradual increase over several years from the beginning of the introduction of the image forming apparatus from May XX to May ZZ.
This is considered a trouble due to wear.
Moreover, according to FIG. 11, periodicity is recognized according to the season. For example, the number of visits decreases from May to August, and increases from 11 to February. There is a difference of about twice between June and January.
This is considered to be due to the influence of ambient environmental information (temperature and humidity), which also affects the temperature and humidity in the image forming apparatus, and the number of occurrences of paper feeding troubles changes.

In such a state, a failure prediction diagnosis system as shown in the first embodiment, in particular, further classified into categories according to failure factors for each of the environmental classifications A to I, for example, failure risk of paper feed trouble / image quality trouble When the regression formulas of (1) and (2) were selected and selected, and the respective failure risks were plotted over a predetermined number of days, results as shown in FIG. 12 were obtained.
In the figure, for example, when the threshold for whether or not a service engineer (service man) is dispatched is 60%, a service engineer will be dispatched in a paper feed trouble on February 16, and maintenance and inspection work will be performed by the service engineer. Is called.
In this situation, on the next day, since the maintenance inspection work by the service engineer is completed, the failure risk is 0.
As described above, according to the present embodiment, before a failure occurs, a service engineer (service man) can visit a user to perform a maintenance check. In other words, it is possible for a service person to make a visit plan and visit the user efficiently for maintenance, so that the service cost is greatly reduced, the downtime of the image forming apparatus is greatly reduced, and further, the customer It is understood that it leads to improvement of satisfaction.

It is explanatory drawing which shows the outline | summary of the failure prediction diagnostic system of embodiment model to which this invention is applied. It is explanatory drawing which shows the failure prediction diagnostic system which concerns on Embodiment 1. FIG. 1 is an explanatory diagram showing an overview of an image forming apparatus used in Embodiment 1. FIG. 2 is an explanatory diagram illustrating an example of internal information of an image forming apparatus used in Embodiment 1. FIG. It is explanatory drawing which shows the operation | movement process of the failure prediction diagnostic system which concerns on Embodiment 1. FIG. It is a flowchart which shows an example of the regression type preparation process of FIG. 6 is an explanatory diagram illustrating an example of an environment classification of a plurality of regression equations used in the first embodiment. FIG. It is explanatory drawing which shows an example of the logistic regression analysis used when producing the regression type used in Embodiment 1. FIG. It is a flowchart which shows an example of the service person dispatch necessity determination process of FIG. It is explanatory drawing which shows the failure prediction diagnostic system which concerns on the modification of Embodiment 1. It is explanatory drawing which shows an example of the relationship between the service man's visit date and the number of visits in an Example. It is explanatory drawing which shows the example of a change of the failure risk in an Example.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Failure prediction diagnostic apparatus, 2 ... Information collection means, 3 ... Regression model setting means, 4 ... Regression model selection means, 5 ... Failure prediction judgment means, 6 ... Display means, 10 (10 (1) -10 (n) ) ... Equipment to be diagnosed, 11 ... Communication network

Claims (15)

A failure prediction diagnosis device for diagnosing a failure prediction state of a diagnosis target device that can be stored together with environmental information including information about temperature and humidity with respect to internal information including information related to an operation state of the diagnosis target device and information used for control ,
An information collecting means for collecting said internal information and the environment information from the diagnosis target equipment,
Wherein said calculating possible regression models based on internal information of the diagnosis target equipment failure prediction index corresponding to the failure predicted state of the diagnostic target device, preset respectively for each environmental compartments separated by a range of temperatures and humidity regression Model setting means;
Based on the information the environment information more collected in collection means, corresponding to the environmental compartments separated by a range of temperature and humidity of the environmental information from a plurality of regression models set in advance by the regression model setting means A regression model selection means for selecting a regression model to be
On the basis of the regression equation as selected regression model in the regression model selecting means calculates the failure predictor of the diagnosis target device by using the internal information of the diagnosis target device collected by the information collecting means, the A failure prediction determination means for determining a failure prediction state of the diagnosis target device based on the calculated failure prediction index ;
A failure prediction and diagnosis apparatus comprising: In the failure prediction and diagnosis apparatus according to claim 1,
The failure prediction diagnosis apparatus, wherein the diagnosis target device is an image forming apparatus that forms an image on a recording material to be conveyed. In the failure prediction diagnostic apparatus according to claim 1 or 2,
The failure prediction index, the diagnosis fault risk that indicates the danger degree leading to failure of the target device is any one of the fault severity indicating the degree of the symptoms of failure urgency and fault indicating an emergency degree for the failure A failure prediction and diagnosis device characterized. In the failure prediction diagnostic apparatus according to any one of claims 1 to 3,
The failure prediction diagnosis apparatus, wherein the regression model setting means sets a plurality of regression models using logistic regression analysis. In the failure prediction diagnostic apparatus according to any one of claims 1 to 3 ,
The regression model setting means includes, as internal information of the diagnostic target device used when setting the regression model, internal information when the diagnostic target device fails and internal information after repairing the failed diagnostic target device A failure prediction diagnosis apparatus characterized by that. In the failure prediction diagnostic apparatus according to any one of claims 1 to 3 ,
The failure prediction diagnosis apparatus is characterized in that the regression model setting means updates a regression equation as a regression model periodically or irregularly in consideration of use conditions and environmental conditions of the diagnosis target device. In the failure prediction diagnostic apparatus according to any one of claims 1 to 3 ,
The failure prediction diagnosis apparatus characterized in that the regression model setting means classifies a regression model created for each environment category into a plurality of failure factors. In the failure prediction diagnostic apparatus according to any one of claims 1 to 7,
The failure prediction determining unit determines a failure prediction state according to which region the calculated failure prediction index corresponds to in advance . The failure prediction and diagnosis apparatus according to claim 8,
The failure prediction judging means variably sets a threshold value at which a failure prediction index is classified. In the failure prediction diagnostic apparatus according to claim 8 or 9,
The failure prediction judging means determines the dispatch of a maintenance inspection specialist under the condition that the failure prediction index exceeds a predetermined threshold value with high urgency. The failure prediction diagnostic apparatus according to any one of claims 1 to 10,
A failure prediction diagnosis apparatus comprising display means capable of displaying a failure prediction state determined by the failure prediction determination means. A plurality of diagnostic target devices that can be stored together with environmental information including information about temperature and humidity with respect to internal information including information related to the operating state of the diagnostic target device and information used for control ;
A failure prediction diagnosis apparatus that is connected via a communication network capable of communicating with each diagnosis target device and diagnoses a failure prediction state of each diagnosis target device ;
A failure prediction diagnosis system comprising:
The failure prediction diagnostic apparatus is
An information collecting means for collecting communicatively said internal information and the environment information from the respective diagnosis target device,
Wherein said calculating possible regression models based on internal information of the diagnosis target equipment failure prediction index corresponding to the failure predicted state of the diagnostic target device, preset respectively for each environmental compartments separated by a range of temperatures and humidity regression Model setting means;
Based on the information the environment information more collected in collection means, corresponding to the environmental compartments separated by a range of temperature and humidity of the environmental information from a plurality of regression models set in advance by the regression model setting means A regression model selection means for selecting a regression model to be
On the basis of the regression equation as selected regression model in the regression model selecting means calculates the failure predictor of the diagnosis target device by using the internal information of the diagnosis target device collected by the information collecting means, the A failure prediction determination means for determining a failure prediction state of the diagnosis target device based on the calculated failure prediction index ;
A failure prediction diagnostic system comprising: The failure prediction diagnosis system according to claim 12,
The information gathering means periodically collects internal information from each diagnosis target device together with environmental information, and is a failure prediction diagnosis system. The failure prediction diagnosis system according to claim 12 ,
The information collection means collects together diagnosis target information for identifying a diagnosis target device, internal information of the diagnosis target device, and information collection date and time information for collecting environmental information. . Computer used in a failure prediction diagnosis apparatus for diagnosing a failure prediction state of a diagnosis target device that can be stored together with environmental information including information about temperature and humidity with respect to internal information including information related to the operation state of the diagnosis target device and information used for control The
Information collecting means for collecting the internal information and the environmental information from the diagnosis target device;
Regression in which a regression model capable of calculating a failure prediction index corresponding to the failure prediction state of the diagnosis target device based on the internal information of the diagnosis target device is set in advance for each environment category divided by temperature and humidity ranges Model setting means;
Based on the environmental information collected by the information collecting means, it corresponds to the environmental classification divided by the temperature and humidity ranges of the environmental information from a plurality of regression models preset by the regression model setting means A regression model selection means for selecting a regression model;
Based on the regression equation as the regression model selected by the regression model selection means, calculate the failure prediction index of the diagnosis target device using the internal information of the diagnosis target device collected by the information collection means, A failure prediction determination means for determining a failure prediction state of the diagnosis target device based on the calculated failure prediction index;
Failure prediction diagnostic program to make it function.

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