JP2534396B2 - Self-diagnosis and self-healing system for image forming apparatus - Google Patents

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a self-diagnosis and self-repair system for an image forming apparatus. More specifically, the present invention relates to an apparatus and system capable of self-diagnosing and self-repairing the operating state of an image forming apparatus by utilizing artificial intelligence and knowledge engineering, which have been actively researched in recent years.

<Prior art> In the field of development of precision machinery and industrial machinery, artificial intelligence (so-called AI) has recently been introduced in order to save labor in maintenance work and prolong automatic operation.
Research on expert systems using technology has been actively conducted. Some expert systems self-diagnose whether a failure has occurred in the device and also self-repair the failure that has occurred.

<Problems to be Solved by the Invention> However, in the conventional failure diagnosis system by the expert system, (a) knowledge is not versatile and failure diagnosis cannot be performed on various objects, (b) unknown failure However, if the target becomes complicated, the amount of knowledge necessary for failure diagnosis will explosively increase, which makes it difficult to implement, and (d) is difficult to acquire knowledge. Was pointed out.

More specifically, a conventional automatic adjustment system or fault diagnosis system basically operates a corresponding actuator based on an output of a certain sensor. That is, a kind of automatic adjustment and failure diagnosis have been performed by a combination of a predetermined sensor and actuator. Therefore, basically, a certain sensor corresponds to a specific actuator, and the relationship between them is fixed.

Therefore, (1) The relationship between sensor parameters and actuator parameters must be numerically specified.

(2) For the reason of (1) above, the relationship between the sensor parameter and the actuator parameter strongly depends on the target, and the versatility is poor. In other words, it cannot be used for various objects.

(3) The relationship between the parameters of each sensor or the parameter of each actuator is irrelevant to the control. Therefore, only simple control based on the relationship between the parameters of the corresponding sensor and the parameters of the actuator can be performed, and failures that can be dealt with are limited in advance.

In other words, at the design stage, possible failures must be predicted and mechanisms for dealing with the failures must be incorporated, and unknown failures cannot be handled.

(4) For the reason of (3) above, it is not possible to predict the secondary effect on the parameters of other actuators that may be caused by operating the parameters of an arbitrary actuator.

There were problems such as.

As described above, in the conventional automatic adjustment system and failure diagnosis system, the predicted failure A is performed based on the set A of the sensor A and the actuator A, and the predicted failure B is performed based on the set B of the sensor B and the actuator B. In other words, the predicted failure C is performed based on the set C of the sensor C and the actuator C, and the failure diagnosis is performed based on the independent set of the sensor and the actuator C, and the failure is repaired based on the diagnosis. Did not.

The present invention has been made in the background of such a conventional technique, and an object thereof is to provide a novel self-diagnosis and self-repair system for an image forming apparatus, which solves the drawbacks of the conventional technique.

A more specific object of the present invention is, in a self-diagnosis and self-repair system for applying a case to perform failure repair, when the case cannot be applied as it is, the necessary preparatory work for applying the case is performed. It is to provide a system that enables flexible application of additional cases. Further, it is to provide a system in which when the repair work is not successful as a result of applying the case, the failure due to the case application is avoided and the success rate of case application is increased. . Furthermore, it is to provide a system for enriching the cases by adding the cases as successful cases when the failure repair by the cases with work is successful.

<Means for Solving the Problems> The present invention is a self-diagnosis and self-repair system for an image forming apparatus that embodies image data and generates a visually recognizable image. Qualitatively expressed by using, reference value data for specifying a range of qualitative values for predetermined parameters among the parameters, and specific parameters among the parameters, conditions that the parameters should be able to take Fault diagnosis knowledge for setting the target model storage means, a plurality of sensor means for detecting and outputting a functional state at a predetermined portion of the image forming apparatus, and outputs of each sensor means to the storage means. Means for converting into qualitative status data by comparing with stored reference value data, converted status data By comparing with the failure diagnosis knowledge stored in the storage means, it is determined whether or not a failure symptom occurs in the image forming apparatus, and when the failure symptom occurs, the failure cause that causes the failure symptom, Fault diagnosis and simulation means for inferring and determining based on the parameter status of the qualitative data, work of the minimum unit is enumerated with work numbers,
Each work is in the form of a rail, and when the antecedent part is operated when the antecedent part is operated, the content of the consequent part is obtained. A plurality of work scripts set for each cause of failure. A work script storage means for storing the status of the device before the failure repair represented by the status of the plurality of parameters, and the work number of the work necessary for the failure repair are described. Case storage means for storing a plurality of cases classified by symptoms and failure causes, based on the failure symptoms and failure causes obtained by the failure diagnosis and simulation means, from the plurality of cases stored in the case storage means, Case detection means for detecting applicable cases, and a work script having the same failure cause as the case detected by the case detection means is stored in the work script memory. Select from reading means, a work corresponding to the work number described in the detected cases from the read-out work script,
By comparing the parameter status obtained by the failure diagnosis and simulation means, the state of the device before the failure repair described in the detected case, and the status of the antecedent part of the read work, the selected work is broken as it is. When the application discriminating means for discriminating whether or not it can be applied to restoration, the application discriminating means discriminates that the selected work can be applied as it is, the first repair executing means and the application discriminating means for executing the work are selected. A means for detecting a preceding work to be performed prior to the selected work from the works listed in the read work script in response to determining that the work cannot be applied as it is, the detected preceding work And second repair execution means, first repair execution means, or second repair execution means for executing the work in the order of the selected work. After the work is performed by the work, the work result judging means for judging whether or not the result to be achieved by the work is obtained, and the work failing in response to the work result judging means judging that the work is unsuccessful. The additional work for avoiding the cause of the above is detected from the works listed in the read work script, and the additional processing means for performing the work and the additional work detected by the additional processing means are executed. A self-diagnosis and self-restoration system for an image forming apparatus, further comprising: a third restoration execution means for performing the selected work again.

According to the present invention, in the self-diagnosis and self-restoration system for the image forming apparatus described above, the work is executed by the first restoration execution means, the second restoration execution means or the third restoration execution means. Whether or not the failure repair is successful is diagnosed by the failure diagnosis and simulation means, and when it is successful, a new case in which the work number for the success and the state of the device before the failure repair are described is created, A self-diagnosis and self-repair system for an image forming apparatus, further comprising: a successful case creating unit that stores the case in a case storing unit.

<Operation> According to the present invention, the output of the sensor means is compared with the reference value data and converted into qualitative state data. Each state data is compared with the fault diagnosis knowledge. As a result, for example, when a certain state data does not satisfy the condition set by the fault diagnosis knowledge, it is determined that the fault symptom is occurring. When it is determined that a failure symptom occurs, each state data is applied to the qualitative data to infer the cause of the failure causing the failure symptom, and the state of the parameter at that time is simulated.

On the other hand, a work script and a case are stored as information necessary for failure repair. The work script is set for each failure.

In each work script, the smallest unit work is listed with a work number. Each work is expressed in a rule format such that when the antecedent part operation is performed in the antecedent part status, the consequent part status is obtained.

Cases are classified according to failure symptoms and failure causes. In each case, the state of the device before the failure repair represented by the status of a plurality of parameters and the work number of the work required for the failure repair are described.

When the failure diagnosis and the failure cause are obtained by the failure diagnosis and simulation means, an applicable case that matches the obtained failure symptom and the failure cause is detected from the cases stored in the case storage means. In addition, a work script having the same failure cause as the detected case is read. Then, the work corresponding to the work number described in the detected case is selected from the work script. It is determined whether the selected work can be directly applied to the failure repair. In this determination, first, the parameter status obtained by the failure diagnosis and simulation means is compared with the state of the device before the failure repair described in the case. If they do not completely match, the parameter status obtained by the failure diagnosis and simulation means is compared with the status of the antecedent part of the read work.

The parameter status obtained by the failure diagnosis and simulation means and the state of the device before the failure repair described in the example do not completely match, and the obtained parameter status does not match the work antecedent status. Sometimes
Since the selected work cannot be applied as it is, the preceding work to be performed prior to the selected work is detected from the works listed in the read work script.

Then, the work is executed and the repair is performed in the order of the detected preceding work and the selected work.

On the other hand, although the parameter status obtained by the failure diagnosis and simulation means and the state of the device before the failure repair described in the example do not completely match, the parameter status and the status of the antecedent part of the work do not match. When
The work is carried out. When work is performed and the work does not achieve the desired result,
It is determined that the work has failed, and additional work for avoiding the cause of the failure is determined. This additional work is detected and determined from the works listed in the read work script. Then, after the additional work is performed, the selected work described above is performed again.

As a result of the repair, if the repair is successful,
A new case in which the detected preceding work, additional work, and selected work are described is created, and the cases are enriched.

<Example> Prior to the description of a specific example, the correspondence between the constituent elements described in the claims and the constituent elements described in the embodiment will be described.

The "target model storage means" in the claims corresponds to the "target model storage unit 14" in FIG.

The "plurality of sensor means" in the claims corresponds to "sensors 1a, 1b, 1c" in FIG.

The "means for converting to qualitative state data" in the claims is the "digital signal / symbol converter 1" shown in FIG.
1 ”is supported.

The "fault diagnosis and simulation means" in the claims corresponds to the "fault diagnosis section 12" and the "fault simulation section 13" in FIG.

The "case storage means" in the claims is "case base storage 17" and "work script storage 18" in FIG.
Is supported.

The "case detection means" in the claims corresponds to the "repair planning unit 15" in FIG.

The "repair execution means" in the claims corresponds to the "repair planning unit 15" and the "actuators 6a, 6b, 6c" in FIG.

Outline of System Configuration FIG. 1 is a block diagram showing the system configuration of an embodiment of the present invention. This system includes a plurality of sensors 1a, 1b, 1c installed on the target machine and a plurality of actuators 6a, 6b, 6c for changing the functional state of the target machine.
It is included.

Each of the plurality of sensors 1a, 1b, 1c is for detecting a change in an element of the target machine or a related state between the machine elements caused by the operation of the target machine. Information taken from each of the plurality of sensors 1a, 1b, 1c is amplified by the amplifier circuit 2, converted from an analog signal to a digital signal by the A / D converter circuit 3, and supplied to the system control circuit 10.

The system control circuit 10 includes a digital signal / symbol conversion unit 11, a failure diagnosis unit 12, a failure simulation unit 13,
A target model storage unit 14, a restoration planning unit 15, and a symbol / digital signal conversion unit 16 are included. Further, a case base storage unit 17 and a work script storage unit 18 are connected to the repair planning unit 15.

The digital signal / symbol converter 11 converts a digital signal supplied from the A / D conversion circuit 3 into qualitative information. That is, a conversion function is provided for converting a digital signal into one of three symbols, for example, normal, high, and low. By converting the signals provided from the sensors 1a, 1b, 1c into such qualitative information symbolized, an approach to fault diagnosis is facilitated. In addition,
The symbols are not limited to three of normal, high and low as in this example, and may be on and off or A, B, C and D
Etc. may be used. When the digital signal is converted into a symbol by the conversion unit 11, the characteristic data unique to the target machine stored in the target model storage unit 14 is referred to. Details of the feature data and signal conversion will be described later.

The failure diagnosis unit 12 and the failure simulation unit 13 determine whether there is a failure by comparing the symbols converted by the digital signal / symbol conversion unit 11 with the failure diagnosis knowledge stored in the target model storage unit 14, Further, it is a component that performs a failure diagnosis, and as a result, expresses and outputs a failure state of the target machine by qualitative information, that is, a symbol.

The repair planning unit 15, the case-based storage unit 17, and the work script storage unit 18 are components for inferring a repair plan and deriving a repair work based on the inference result of the failure diagnosis when there is a failure. . In inferring the repair plan and deriving the repair work, a case regarding past repair success stored in the case base storage unit 17 is searched, and a work script for executing the searched successful case (the repair operation is performed. Is a work script storage unit.
Selected from 18. Further, the qualitative data (described in detail later) stored in the target model storage unit 14 is utilized.

The failure diagnosis unit 12, the failure simulation unit 13, the repair planning unit 15, the case-based storage unit 17 and the work script storage unit 18, the failure diagnosis and failure simulation means, and the method of deducing the repair plan and deriving the repair work, It will be described in detail later.

The repair work output from the repair planning unit 15 is a symbol /
In the digital signal conversion unit 16, the object model storage unit 14
Is converted into a digital signal with reference to the stored information.

Then, the digital signal is converted from a digital signal to an analog signal by the D / A conversion circuit 4 and is supplied to the actuator control circuit 5. Actuator control circuit 5
Selectively operates a plurality of actuators 6a, 6b, 6c based on an applied analog signal, that is, an actuator control command, to execute a repair operation.

FIG. 2 is a flowchart showing the processing of the system control circuit 10 in FIG. Next, an outline of the processing of the system control circuit 10 of FIG. 1 will be described with reference to FIG.

The detection signal of the sensor 1a, 1b or 1c is amplified and converted into a digital signal, and is read into the system control circuit 10 at every predetermined read cycle (step S1).

The read digital signal is symbolized in the digital signal / symbol converter 11 (step S
2). This symbolization is performed based on feature data preset in the target model storage unit 14, that is, reference value data unique to the target machine. For example, the target model storage unit 14 stores, as reference value data specific to the target machine,
The output range of each sensor 1a, 1b, 1c is set as follows.

That is, the sensor 1a: output ka ₁ below = low output ka ₁ ~ka ₂ = exceeded normal output ka ₂ = high sensor 1b: output kb ₁ below = low output kb ₁ ~kb ₂ = exceeded normal output kb ₂ = High sensor 1c: is set the output kc ₁ below = low output kc ₁ ~kc ₂ = normal output kc ₂ excess = high. Digital signal / symbol converter 11
Then, based on the reference value data specific to the target machine set in the target model storage unit 14, the digital signals from the sensors 1a to 1c are converted into symbols of "low", "normal" or "high". .

Next, in the failure diagnosis unit 12, the converted symbols are evaluated, and the presence or absence of a failure is determined and a failure symptom is specified (step S3). The failure diagnosis knowledge stored in the target model storage unit 14 is utilized for determining the presence / absence of a failure and identifying the failure symptom by evaluating the symbol. The fault diagnosis knowledge is, for example, a setting condition that a specific parameter must be normal, for example. If the specific parameter is not normal, it is determined that there is a failure, and the failure symptom is specified by what the specific parameter is. If there is no failure, the routine of steps S1, S2 and S3 is repeated.

If it is determined that there is a failure in step S3,
Inference of the state of the target machine, that is, failure diagnosis and simulation of the failure state are performed (step S4).

Specifically, stored in the target model storage unit 14,
Based on qualitative data that qualitatively expresses the coupling relationship between each element that constitutes the device using parameters, the failure diagnosis unit
In 12, the parameter causing the failure is searched, and in the failure simulation section 13, assuming that the searched parameter is the cause of the failure, the failure state is simulated. Further, in the failure diagnosis unit 12, the simulation result and the current parameter value are compared to judge the correctness of the assumption that the retrieved parameter is the cause of the failure. The above processing is performed on a plurality of parameters to be searched.

As a result of the failure presence / absence determination, the failure diagnosis, and the failure state simulation, the failure symptom and the failure cause of the target machine are determined. Here, the failure symptom is a change in the output status of the target machine (for example, "a copy image is thin" in the case of a copying machine), and the failure cause is a target that causes a change in the symbol. Changes in the mechanism or structure of the machine (for example, in the case of a copying machine, "light reduction of halogen lamp" etc.)
Is.

Next, the repair planning unit 15 searches for a large number of cases stored in the case-based storage unit 17 based on the failure diagnosis and the simulation result of the failure state (step S5). Then, a case close to the current state of the target machine is detected (step S6). The detection of this case is performed based on whether the failure symptom and the failure cause match.

Then, the repair work based on the detected case is executed (step S7). In the repair work, the case is corrected or the repair work is corrected as needed, and the corrected case is registered as a new case.

If the case-based repair work is successful (YES in step S8), the process ends, but if the case-based repair work is not successful (NO in step S8), the repair method is inferred. (Step S9) Further, the simulation of the secondary effect is performed (step S10), the repair plan is determined, and the repair work based on the determination is executed (step S11).

Inference and work execution in steps S9-S11
Although not based on a case, when the repair work based on this inference is successful, the repair result is registered in the case base storage unit 17 as a new case.

Next, a method of failure diagnosis and failure repair will be described in detail with reference to specific examples. In the explanation below,
As an example, a method in which the peripheral portion of the photosensitive drum in a small-sized ordinary copying machine is used as a target machine will be described.

Description of Specific Target Machine as an Example Configuration and State of Target Mechanism FIG. 3 is an illustrative view showing a specific target machine.
In FIG. 3, reference numeral 21 denotes a photosensitive drum, 22 denotes a main charger, 23 denotes a halogen lamp for illuminating a document, 24 denotes a developing device, and 25 denotes a transfer charger.

In this embodiment, for example, three sensors 1a, 1b, 1c are provided. That is, the sensor 1a is an AE sensor for measuring the amount of light incident on the photosensitive drum, the sensor 1b is a surface potential sensor for measuring the surface potential of the photosensitive drum, and a sensor.
Reference numeral 1c denotes a densitometer for measuring the density of an image copied on paper.

Although not shown in FIG. 3, three types of actuators are provided. That is, a main charging volume VR1 for changing the main charging voltage of the photosensitive drum, and a lamp volume AV for controlling the light amount of the halogen lamp.
Three volumes, R and a transfer volume VR2 for controlling a transfer voltage between the photosensitive drum and the copy sheet, are provided as actuators.

By the way, when the target machine shown in FIG. 3 is viewed from a physical point of view, the target machine is expressed as a combination of a plurality of elements at the entity level, and the connection relationship between the elements is qualitatively expressed using parameters. , As shown in Table 1. The expression format shown in Table 1 will be referred to as a "substance model". Here, “qualitatively” means a relationship between A and B, for example, “if A is increased, B is also increased”. Specifically, taking the exposed part in Table 1 as an example,
The equation X = H _L -D indicates a qualitative relationship between parameters such as “X rises when H _L rises or D falls”.

Further, the representation of FIG. 4, which is an abstraction of the entity model and is expressed as a connection tree of each parameter, is called a “mathematical model”.

Then, the "physical model" and the "mathematical model" are collectively referred to as the "target model". "Target model"
This is qualitative data common to image forming apparatuses, which is also used for repair of a failure described later.

The contents of the substance model and the mathematical model as qualitative data are stored in the target model storage unit 14.

Further, the target model storage unit 14 includes, for a predetermined parameter among the parameters included in the real model,
For example, reference value data measured at the time of factory shipment is stored. The reference value data is characteristic data unique to this image forming apparatus.

For example, in this machine, as in FIG. 5, the parameters X, V _s, O _s, for V _n, respectively, low, normal, reference value data specifying the ranges of high are stored.

In this embodiment, the above reference value data can be updated in response to sensing data in the subsequent failure diagnosis and failure repair process, changes in the operating state of the machine, and the like.

Further, the target model storage unit 14 stores, based on the converted symbols, evaluation function knowledge as an example of failure diagnosis knowledge serving as a reference for determining whether or not the target machine is operating normally. ing.

Note that the evaluation function knowledge, in other words, the failure diagnosis knowledge, may be specific to the target device, may not be specific, and may be common to a wide range of image forming apparatuses.

 The evaluation function knowledge includes the following knowledge.

'Here ≦ normal separation performance S _P ≦ normal, O _s, O _s' image density O _s = normal fog degree O _S, when S _p is not above conditions, that the objective machine is not working properly Become.

Now, consider a case where the digitized sensor information of the target machine in the normal operation has the following value.

AE sensor value X = 30 Surface potential sensor value V _s = 300 Densitometer value O _s = 7 Also, densitometer value when using a blank original with optical density D = 0
O _s = fogging degree O _s ′, value of surface potential sensor with halogen lamp off
It is assumed that V _s = dark potential V _n , and those values are fog degree O _s ′ = 50 and dark potential V _n = 700, respectively.

The fog degree O _s ′ and the dark potential V _n were measured as
It may be performed manually, or may be programmed to be automatically measured by the sensor under a certain condition, for example, every time the power of the target machine is turned on, or before the start of copying. In this embodiment, the latter is adopted.

AE sensor 1a, the value X obtained by the surface potential sensor 1b and the densitometer _{1c, V s, O s,} O S ', V n , respectively, are converted into symbols in the digital signal / symbol conversion section 11.

The conversion is performed by comparing a digital value given from each sensor 1a, 1b or 1c with reference value data as feature data stored in the target model storage unit 14 as described above. High or low 3
Converted to any symbol of any kind.

In this embodiment, each parameter is symbolized as follows.

X = high V _s = low O _s = low V _n = normal In the fault diagnosis unit 12, each of these symbolized parameters is an evaluation function as an example of fault diagnosis knowledge stored in the target model storage unit 14. Compared to knowledge.
As a result, since the image density O _s is not normal, it is determined fault is with the fault condition is determined to be "image density too low (O _s = low)". Then, next, with "O _s = low" as the failure symptom, the failure diagnosis, that is, the cause of the failure is inferred.

Fault Diagnosis Method The fault diagnosis is first performed in the fault simulation unit 13 using the mathematical model shown in FIG. 4, and a parameter that may cause O _s = low is searched for.

A mathematical model in FIG. 4, when pointing out parameters that may reduce the O _s, as shown in Figure 6. In FIG. 6, a parameter with an upward arrow or a downward arrow is a parameter that may cause a parameter O _s = low, and a parameter with an upward arrow indicates that of a downward arrow when the parameter increases. Causes O _s = low if its parameter drops.

Next, each parameter ζ, D _s , V _t , γ _o , V _b , V that may cause O _s = low searched for in the mathematical model.
_With respect to _s , V _n , X, β, H _L , D, the cause of the parameter change is detected by the failure diagnosis unit 12.

This detection is performed based on the substantial model in Table 1,
In this embodiment, the following failure cause candidates are inferred. That, V _t = low: → transfer transformer failure zeta = low: → paper deterioration V _b = high: → phenomenon bias defective gamma _o = low: → deterioration of toner V _n = low: → improper principal charge voltage H _L = High: → Halogen lamp setting error D = Low: → Original is thin V _t = Low indicates a transfer transformer failure, ζ = Low indicates paper deterioration, V _b = High indicates development bias Is the failure cause knowledge, and this knowledge is included in the qualitative data common to the image forming apparatuses.

Among the parameters, β is the sensitivity of the photoconductor,
It is excluded because it does not rise. D _s , V _s and X are also excluded because they are represented by other parameters.

Then, a failure simulation is performed in the failure simulation unit 13 with respect to the above inference made in the failure diagnosis unit 12.

The failure state simulation is to infer the state of the target machine when the inferred failure occurs, respectively. More specifically, it is assumed that the cause of O _s = low, that is, the failure cause is, for example, a defect of the transfer transformer, and V _t = low is set for a model in a normal state. Then, the influence given to each parameter in that state is examined on a mathematical model. If V _t = low is set, then O _s = low and _Sp = low, and all other parameters are normal, which contradicts X = high and V _s = low from the sensor.
Therefore, the result that the inference of the failure cause is incorrect is obtained.

Similarly, ζ = low is set on the mathematical model in the normal state, and the result is compared with the symbol obtained from the sensor. Also in this case, the symbol from the sensor is X = high while X = normal on the mathematical model, so there is a contradiction, and the inference of the cause of the failure is determined to be erroneous.

In this way, the simulation of the failure state is performed for all the failure cause candidates, and it is confirmed whether or not the inference of the failure cause is correct.

As a result, in the case of this example, when the cause of the failure is “halogen lamp setting failure ( _HL = high)”, a result that matches the actual state of the target machine is obtained, and other failures It is concluded that all possible causes contradict the actual state of the device.

Therefore, it can be concluded that the cause of the failure in this case is a defective setting of the halogen lamp. Table 2 shows the state of each parameter of the target machine at that time.

When the states of the parameters shown in Table 2 are traced on the mathematical model, FIG. 7 is obtained. In FIG. 7, the downward arrow attached to the right side of each parameter is low, the upward arrow is high, and N is normal.

Execution of repair work Next, the failure diagnosis unit 12 and the failure simulation unit 13
Based on the result of the failure diagnosis performed in step S1, repair work is executed according to the flowcharts shown in FIGS. 8A, 8B and 8C.

Hereinafter, the repair work will be described step by step according to the flow charts shown in FIGS. 8A, 8B, and 8C.

Note that the flowcharts of FIGS. 8A, 8B, and 8C correspond to steps S5, S6, S7 in the flowchart of FIG.
It corresponds to S8 and S8, and specifically and in detail shows the contents of the restoration process.

Searching for a case In accordance with the above-mentioned failure diagnosis method, the cause of the failure causing the failure symptom is inferred (step S2).
1). Based on the result, the case-based storage unit 17 (first
A large number of cases stored in (see figure) are searched, and cases that can be used for repair are detected (steps).
S22).

More specifically, for all the cases stored in the case-based storage unit 17, as shown in Table 3, the case number, the status of parameters before repair, the status of parameters after repair,
The failure symptom, failure cause, repair work, number of successful applications and number of unsuccessful applications are recorded.

In addition, each case is
It is classified hierarchically.

Therefore, the repair planning unit 15 determines that the failure symptom “image density is too low (O _s = low)” and the cause of failure “halogen lamp setting failure (H _L = high)” diagnosed by the failure diagnosis unit 12 and the failure simulation unit 13. ) ”As an index, a case satisfying both the failure symptom and the failure cause is detected. Therefore, even if the failure symptom is "low image density", the failure cause is not detected, for example, "defective main charging voltage".

Here, the "fault symptom" is, as described above, a phenomenon that is recognized as a defect of the target machine such as "low image density" or "image fog". Indicates a change in the function or structure of the target machine such as "a setting failure of the halogen lamp" or "a failure of the main charging voltage".

Now, as a result of searching for cases based on the failure symptom “image density is too low” and the failure cause “halogen lamp setting failure”, cases (1) to Tables 4 to 6 below are shown.
It is assumed that (3) is detected.

By the way, since there are a plurality of detected cases, three in this case, it is necessary to decide which case is to be applied to the repair work first.

Then, priority is given to the detected three cases (1) to (3) (steps S23 to S25). The priority order regarding the application order is compared with the status of the parameter before repair in each case and the current status of the parameter of the target machine simulated in the failure diagnosis (see Table 2) (step S23), and The order of priority is given to the cases with the largest number of parameters.

Specifically, comparing the status of the parameters before restoration in each of the cases (1) to (3) with the current status of the parameters (Table 2), only the state of V _n is different in the case (1). In case (2), the states of V _n and V _t are different. In case (3), the states of V _n , V _b and V _t are different.

Therefore, the order of priority is given to the order of case (1), case (2), and case (3).

If the number of coincidences between the parameter status before restoration and the current parameter status is equal, the successful application number is taken into consideration (step S24), and the higher the successful application number, the higher the priority.

Furthermore, when the number of matches between the parameter status before repair and the current parameter status is the same and the number of successful applications is the same, the number of failed applications is taken into consideration (step S24), and the smaller the number of failed applications, the higher the priority. Rank is given.

Of course, when only one case is detected in step S22, the prioritization of the application order is omitted.

Application of Case Next, a repair plan is executed based on the case of the first priority (when only one case is detected, the detected case).

In executing the restoration plan, first, the case (1) with the first priority is set in, for example, the work register, and the work stored in the work script storage unit 18 based on the case (1). A work script having a failure cause "halogen lamp setting failure" is selected from the scripts and is set in the work register (step S26).

Table 7 shows an example of a work script for a defective halogen lamp setting.

As shown in Table 7, in the work script, the failure cause "halogen lamp setting failure" which is an index is described, and a plurality of works 1, 2, 3, ... Are listed.
Each work is described in a rule format and consists of antecedent part status, antecedent part operation and consequent part status. Each work is
This means that when the antecedent part operation is performed in the antecedent part status, the consequent part status is obtained.

More specifically, for example, in the case of work 1, the condition of the antecedent part is the state of the parameter H _L = high, and in this state, by performing the antecedent part operation of lowering the lamp volume AVR, Parameter H _L
= Parameter change of "normal", that is, the consequent situation is obtained.

The work script is set for each cause of failure,
The smallest unit of work is listed. Since work scripts are set for each failure cause, there are as many work scripts as there are failure causes.

When the case (1) and the work script of Table 7 are set in the work register (step S26), the status of the parameters before the repair of the case (1) set in the register by the repair planning unit 15 is as follows. It is confirmed whether or not the conditions of the current parameters are completely matched (step S2).
7).

If the status of the parameter before restoration in case (1) completely matches the situation of the current parameter, the work with the number described in the repair work column of case (1) is
It is selected and executed from the "halogen lamp setting error" work script (step S28) (actually, the situation of the parameters in case (1) completely matches the situation of the current parameters shown in Table 2. Therefore, the actual processing proceeds to step S27 → S34, as described later).

When the consequent part status is obtained as a result of the work being performed, it is determined that the work is successful (YES in step S29), and it is further determined whether or not there is the next work (step S3).
0). If there is the next work number in the repair work column, that work is selected from the work script and executed (step S2
8), it is determined whether or not the work is successful (step S29).
The above process is repeated.

Then, when there is no next work (step S3
If NO, the numerical value in the application success number column in the case is incremented by 1 and the success number is registered (step S31).

Even if the work is executed, if the consequent part status of the work is not obtained, it is determined that the work has failed (N in step S29).
O), the value in the application failure number column is incremented by 1 and the failure number is registered (step S32).

Then, it is judged whether or not there is a case of the next priority (step S33), and if there is (YE in step S33).
S), the process from step S26 is performed on the case of the next priority.

If there is no case with the next priority (N in step S33)
O), the restoration plan is inferred in consideration of the secondary effects, which will be described later, that is, the QMS process is performed (step S34).

Then, it is determined whether or not the QMS processing is successful (step S35), and when it is determined that the QMS processing is successful (YES in step S35), a new case is created based on the data obtained by the QMS processing. The case is registered in the case base storage unit 17 (step S36). Then, the process ends.

When QMS processing is unsuccessful (N in step S35
O), the new case is not registered and the process ends.

As described above, the parameter status before restoration in case (1) does not completely match the current parameter status, and the status of the parameter V _n does not match. Therefore, the actual process is step S27. If NO is determined, the process proceeds to step S37 shown in FIG. 8B.

In step S37, the work with the number described in the repair work column of case (1) is specified from the "halogen lamp setting failure" work script. That is, work 1 is designated. Then, the condition of the antecedent part of the work 1 is compared with the current condition of the parameter, and it is determined whether or not they match (step S38).

When performing work, the current parameter status must match the work antecedent status. In this specific example, both in the antecedent part situation of the work 1 and in the current parameter condition, the parameter H _L = high, so the antecedent part situation coincides with the current parameter condition. If they match (YES in step S38), the work 1 is executed (step S39). Then, the success or failure of the work is discriminated (step S40), and when the consequent part status is obtained as a result of the work execution, it is determined that the work is successful (YES in step S40).

Further, the presence or absence of the next work is determined depending on whether or not the next work number is described in the repair work column of the case (1) (step S41), and if there is the next work (in step S41, YES), the next work is designated, the condition of the antecedent part of the work is compared with the current parameter condition (step S37), and the processes from step S38 onward are repeated in the same manner as described above.

If there is no next work (N in step S41)
O), flag A or B (discussed later about what these flags A and B are for) will be determined (step S42), and any flag A will be B.
If not set (NO in step S42), the value in the application success number column of case (1) is incremented by 1 and the success number is registered (step S43), and the process ends.

In other words, in step S27, even if it is confirmed that the status of the parameter before repair of the case does not completely match the status of the current parameter, the work described in the case can be changed, The work described in the case can be performed without adding work, and when the work is successful, the number of successful cases is increased. This is because the restoration process using a case refers to the case closest to the current state and infers the restoration work based on that case, so that the parameter states do not necessarily have to be exactly the same. .

However, in step S39, even if the lamp volume AVR, which is the antecedent operation, is lowered, the parameter H _L does not change to normal as a result, and even if the lamp volume AVR is lowered to its lower limit, the consequent portion If the condition parameter H _L = normal is not obtained, it is determined that the work has failed (N in step S40).
O).

In other words, if the parameter status resulting from a certain work (current parameter status after the work) does not reach the parameter status set in the repair work (consequent part status), the work is judged to have failed. To be done.

Then, at this time, the second additional processing for avoiding the cause of the work failure as described below with reference to the flow of FIG. 8C is executed.

That is, first, the failure symptom "The image density is too low (O _s
= Low) "and the cause of failure is" Halogen lamp setting error ( _HL = High) ", and in the cases, the work number determined to be unsuccessful, for example, Work 1 All cases in which the number is written are detected (step S49). Then, the parameter status before repair of all the detected cases and the current parameter status are compared (step S50), and the difference between the parameter status before repair and the current parameter status is obtained. Common parameters are detected.
That is, the difference between the common parameter situation before restoration of all cases and the current parameter situation is detected (step S51).

In the specific example, since the case where the work 1 is described in the repair work column is the cases (1) and (2), the parameter status before the repair and the current parameters of the cases (1) and (2) are described. The conditions are compared with each other, and the parameter V _n is taken as a common difference. That is, in the parameter situation before restoration, the parameter V _n
= Low, but in the current parameter situation, the parameter V _n = normal.

If it is determined that there is a different parameter (YES in step S51), that parameter, that is, the parameter V _n = normal in the specific example, is hypothesized to be the cause of the failure of this work, and this parameter V _{n is set} to normal. The work that can be changed to low is searched from the work script (step S52), and the presence or absence thereof is determined (step S5).
3).

Looking at the work script in Table 7, by work 5,
Since it is known that the parameter V _n can be changed from normal to low, it is determined that there is work (YE in step S53).
S).

Then, in this case, the work column of the case (1) is provisionally corrected and the work 5 is inserted. Further, the flag B is set to indicate that this temporary correction is made (step S5).
Four). Next, work 5 is executed (step S55).

Then, as a result of the work 5 being executed, when V _n = low, it is determined that the work is successful (YES in step S56).

In this case, V _n = Rho is an indispensable condition for the antecedent situation of task 1. Therefore, the work script shown in Table 7 is corrected so that V _n = law is added to the antecedent part situation of work 1, and the work script of Table 7 is rewritten to that shown in Table 8 (step S57). .

In the work script shown in Table 8, antecedent circumstances of the work 1 is "in the H _L = high, and, V _n = low".

Then, the processing from step S37 shown in FIG. 8B is performed again. In this specific example, if work 1 is performed and it succeeds (YES in step S40), there is no further work to be performed (NO in step S41), and flag B is set.
Is set (Y in step S42)
ES). Therefore, a new case (1-1) is created and registered based on the parameter status and processing at this time. Further, the flags A and B are reset (step S44). Table 9 shows this new case (1-1).

The case (1-1) shown in Table 9 is different from the case (1) shown in Table 4 in that the parameter V _n = normal in the parameter status before the repair and the repair work column , "5,1" is described. The number of successful applications of the case (1-1) is only once this time, and the number of unsuccessful applications is 0.

Now, in step S56 of FIG. 8C, when it is determined that the work was not successful, it is determined whether or not there is another work in the work script that can change the parameter V _n from normal to low. If there is work (step S58), the processing from step S54 is performed.

Furthermore, if it is determined in step S51 that there is no different parameter, or if it is determined in step S53 that there is no work, after the flags A and B are reset (step S59), another case, that is, It is determined whether there is a case with the next application priority (step
S60).

Then, if there is the following case (YE in step S60)
S), the case and the corresponding work script are set in the work register (step S61), and the processes from step S37 shown in FIG. 8B are performed.

On the other hand, if it is determined in step S60 that there is no case of the next application priority (in step S60, N
O), the process proceeds to step S34 shown in FIG. 8A, and the QMS process is performed.

Next, in the above specific example, consider a case where the repair plan based on the case (1) fails and the repair plan based on the case (2), which is the next application priority case, is performed.

In this case, in step S61, the failure cause “halogen lamp setting failure” work script selected corresponding to case (2) and case (2) is set in the work register.

Then, "1" displayed in the column of case (2) restoration work
Specifies the work 1 of the work script and compares the condition of the antecedent part with the current condition of the parameter (step
S37), it is determined whether the two match or mismatch (step S38). As is clear from the comparison between Table 7 and Table 2, the antecedent situation of work 1 and the current parameter situation are:
Since _HL = high, and there is a match, work 1 is executed (step S39).

Then, when H _L = normal, it is determined that the work is successful (YES in step S40), and the presence or absence of the next work is determined (step S41).

In case (2), since the work 2 exists as the next work, the process proceeds to step S37, the next work 2 is designated, and the condition of the antecedent part and the current parameter condition are compared. As a result, it is determined that the antecedent condition V _t = low of the work 2 does not match the current parameter state V _t = normal (NO in step S38).

As described above, when the work is executed, the current parameter status must match the work antecedent status. Therefore, the first addition process is performed. That is, it is searched whether or not another work for matching the current parameter status with the antecedent status is present in the work script shown in Table 7 (step S45).

Looking at Table 7, it can be seen that task 4 allows the parameter V _t to be low when H _L = normal. Therefore, YES is determined in the step S46, and the process is a step
Proceed to S47. Then, the repair work column of the case (2) is provisionally corrected to "1, 4, 2", and the flag A is set to indicate that the provisional correction has been made (step
S47).

Next, the work 4 added by the temporary correction is executed (step S48), and it is determined whether or not the work 4 is successful (step S40).

If the work 4 is successfully executed (step S40
YES), it is determined whether or not there is next work (step S4).
1). In case (2), since the work 2 exists as the next work, the process proceeds to step S37 again, the next work 2 is designated, and the status of the antecedent part and the current parameter status are compared. As a result, the current parameter status is in agreement with the antecedent status of task 2 because V _t = low due to the execution of task 4 in step S48.

Therefore, YES is determined in the step S38, and the work 2 is executed (step S39).

Then, it is determined whether or not the work 2 is successfully executed (step S40), and if it is successful, it is determined in step S41 whether or not the next work is performed.

In case (2), since there is no next work, the process proceeds to step S42. Then, it is determined that the flag A is set (YES in step S42), and the case where the repair work field is provisionally corrected in step S47 is a new case (2-
It is registered as 1) (step S44). Further, the flags A and B are reset (step S44).

Table 10 shows the case (2-1) that is additionally registered.

The case (2-1) shown in Table 10 is the case (2) in Table 5
Is different from the parameter in the situation before repair.
That it is V _n = normal, it is that the repair work is "1,4,2". In addition, the number of successful applications is 1 this time,
The number of application failures is 0.

If it is determined in step S46 that there is no other work that can match the current parameter status with the antecedent status (NO in step S46), the process proceeds to 8C.
The process proceeds to step S59 shown in the figure.

It is particularly effective for the apparatus such as the small-sized ordinary copying machine as described in the above specific examples to employ the method of searching for cases and applying cases described above in executing the repair work.

This is because an apparatus represented by a small-sized ordinary copying machine has an unstable element (for example, positively utilizing chemical changes) as a control target in its constituent system. Therefore, the relationship between the sensor parameter and the actuator parameter may change due to changes in the state in which the constituent system is placed, such as environmental changes and structural deterioration. The case search in the description of the above specific example can be said to be because the device collects the changes between the parameters during running, causes a kind of learning using the changes, and tunes the knowledge. Even if there is a change between the parameters, the repair work can be effectively performed.

That is, when the relationship between the parameters of the target machine changes, the case is modified based on which change to create a new case, and the content of the work script is modified.

In the above description, the case and the work script are separately stored, and are separately selected and set.

However, instead of such an embodiment, for example, by storing the work itself instead of the work number in the work script in the repair work column in each case, the specific work to be executed is stored. It can be a case that has been done.

In other words, the case and the work script can be integrated.

Inference of Repair Plan Next, inference of repair plan, that is, step S5 in FIG.
The QMS process shown in 1 will be described.

As a result of the failure determination, “image density is too low (O _s = low)” was picked up as a failure symptom, so the goal of repair is to increase O _s .

Therefore, from the relationship on the mathematical model shown in Figure 4, or increasing the D _s, or increase the V _t, or, depending raising the zeta, when it is possible to increase the O _s is repaired target Inferred.

Next, when making inferences with the goal of increasing D _s , V _s
The conclusion is either to increase, or to decrease V _b , or to increase γ _o . In this way, by repeating the inference based on the mathematical model, candidates for the repair operation can be obtained on the mathematical model. The results obtained are shown in Table 11.

By the way, some of the restoration candidates obtained based on the mathematical model can be realized and others cannot be realized. For example, D: the optical density of the original cannot be changed, and β: the sensitivity of the photoconductor is hard to change.

γ _o : The sensitivity of the toner cannot be changed, and ζ: the sensitivity of the paper cannot be changed.

Further, in this specific example, V _b : bias voltage cannot be changed because there is no actuator. Of course, _Vb can be made variable by adding an actuator.

Further, X: logarithm of reflected light amount of the original V _s : surface potential of the drum after exposure D _s : toner density on the drum cannot be changed by itself, and other parameters can be changed indirectly. It can only be changed by, and is excluded from the repair candidates here.

Although not directly related to this specific example, the amplitude of _Asp : AC voltage for separation can also be changed by adding an actuator.

Depending described above, in this embodiment, as the repair candidates, V _t: transfer voltage V _n: surface potential after principal charge H _L: logarithm of halogen lamp output quantity of light is taken up.

On the other hand, the following knowledge is stored in advance in the target model storage unit 14 as repair plan knowledge. That is, (a) increase V _t → increase transfer transformer control voltage (b) decrease V _t → decrease transfer transformer control voltage (c) increase V _n → increase main charging transformer control voltage Raise (d) Decrease V _n → Decrease the control voltage of the main charging transformer (e) Raise H _L → Shift the halogen lamp control signal to the high voltage side (f) Decrease H _L → Halogen lamp control Shift the signal to the low voltage side.

Is. The repair plan knowledge stored in the target model storage unit 14 is characteristic data unique to this device. By applying the repair plan knowledge to a repair candidate obtained based on a mathematical model, as a repair operation for increasing O _s , (a) increase V _t → increase the control voltage of the transcription transformer (c ) There are three methods of increasing V _n → increasing the control voltage of the charging transformer (f) decreasing H _L → shifting the halogen lamp control signal to the low voltage side.

If simply raising the image density O _s, these three
Any of the methods can be repaired.

However, the target machine, by increasing the image density O _s, it is conceivable to undergo various side effects. Therefore, in this embodiment, as described below, inference of the secondary effect is performed based on a mathematical model.

Inference of secondary effects When the three restoration plans derived in the inference of restoration plans are developed on a mathematical model, Figs. 9 to 14 are obtained. That, (a) when increasing the V _t is Fig. 9 and FIG. 10 (FIG. 10 O _S 'in the case of a D = 0 is represented by a mathematical model), (c) _If you increase V _n ,
Fig. 11 and Fig. 12 (Fig. 12 shows _OS 'when D = 0)
Is expressed on the mathematical model), and (f) H _L is lowered, Fig. 13 and Fig. 14 (Fig. 14 shows O _s ' on the mathematical model when D = 0). It is represented).

Then, if the function is evaluated based on the mathematical model, the next state is inferred.

That is, (1) When V _t is increased (FIGS. 9 and 10) (a) The output image density is increased.

(B) When D = 0, O _s ′> normal.
That is, fogging may occur.

(C) S _p > normal, and there is a possibility that defective separation will occur.

(2) When V _n is increased (FIGS. 11 and 12) (a) The output image density increases.

(B) When D = 0, O _s ′> normal and fogging may occur.

(3) When H _L is lowered (Figs. 13 and 14) (a) The output image density only increases, and there are no other secondary effects.

Therefore, the repair plan unit 15 selects a repair plan that has the least side effect, that is, a repair plan that lowers _HL . This repair plan is consistent with the operation for eliminating the cause of failure obtained by the failure diagnosis.

In other words, from a different point of view, inferring the cause of the failure in the failure diagnosis is performed by tracing the actual state when the device has failed on a mathematical model and grasping the state of each component when the device has failed, While the cause of failure was estimated, the repair plan estimation traces the state of the device on a mathematical model based on the assumption that the device is normal and not the fault state, and repairs based on that. Inferring a plan.

In the above-described specific example, the same failure cause and repair plan are obtained as a result of inference in failure diagnosis and inference in repair plan.

However, in some cases, the inference of the fault diagnosis is based on the equipment in the failed state, whereas the inference of the repair plan is based on the equipment in the normal state. . In such a case, by selecting only those that do not contradict the conclusion obtained in the inference process of the failure diagnosis when inferring the repair plan, the inference processing of the repair plan can be performed in a shorter time.

In the above case, if the repair plan of lowering _HL cannot be selected, for example, if the volume of the AVR for shifting the halogen lamp control signal to the lower voltage side is already at the lower limit, the secondary repair plan is selected that increases the V _n of effects less (2).

However, when the repair plan to increase V _n is selected, the side effect of the possibility of fogging is predicted, so in the mathematical model of Fig. 12,
Which parameter should be manipulated to lower O _s ′ is examined based on the mathematical model in FIG. 12,
Moreover, the operation is selected based on the repair plan knowledge. As a result, either raise the H _L, or lowering the V _n, or lowers the V _t is selected, repair plan including preventing the fog generating is performed.

That is, as shown in Fig. 15, the reasoning of the repair operation is developed by assuming the side effect. In the inference expansion of the repair operation as shown in FIG. 15, (a) the branch that is inconsistent with the previous repair plan on the mathematical model is not selected (b) the one with the least side effect is selected (c) ) The expansion that is based on the knowledge that the one that forms the loop stops the expansion at that point.

In Fig. 15, after all, two repair plans remain: (1) V _n ↑ → H _L ↑ → V _n ↑ loop, and (2) V _n ↑ → V _t ↓ → V _n ↑ loop.

Now, suppose that when the loop of (1) is performed as a repair plan, the image density becomes appropriate, that is, O _s becomes normal. In such a case, since V _n and H _L are increased, the value of the surface potential measured by the sensor 1b in the state before restoration in which the image density O _s has returned to the normal value is higher than the value measured first. It should have changed to a fairly high one. However, since not repair work is successful, the state of the parameter V _s of the repaired must be symbolized to normal. Therefore, in such a case, when the restoration is completed, the reference data for symbolizing the parameter V _s shown in FIG. 5 is changed based on the measurement value measured by the sensor 1b.
It is rewritten with the data shown in the figure.

In this way, the reference data is updated as needed after the repair work is completed.

In this embodiment, the aforementioned (1) in FIG.
Specifically, when the main charging volume VR1 is operated to raise the surface potential of the photosensitive drum 21 and the resulting copy is fogged, the lamp volume AVR is operated and the halogen The light intensity of the lamp is increased, and the image density of the copy is reduced.

Then, while raising appropriately alternately main charging volume VR1 and the lamp volume AVR, when the image density becomes normal, that it is obtained from the detection output of the densitometer which is the sensor 1c that the parameter O _s becomes normal Then, the restoration process ends.

Further, if the above two remediation plans are not feasible,
In addition, the repair plan of increasing V _{t in} (1) above is selected, and the fault diagnosis is performed assuming that there are two side effects, fogging occurrence and separation failure, and the repair plan is selected. It

Then, the selected restoration plan is performed, and in the case of loop processing, it is determined that the operation of a parameter on the loop has failed when the operation reaches a limit.

Further, in the case of this embodiment, as described in the specific example, the end of repair is determined when O _s becomes normal,
The repair is stopped in that state.

In the inference of the secondary effects described above, the three repair plans derived in the inference of the repair plan are sequentially developed on a mathematical model, and the secondary effects are inferred together for each of the three repair plans. ing.

Instead of inferring such side effects, the following processing may be performed.

That is, it is assumed that, for example, three repair plans are derived in the inference of the repair plan. In that case, only one of the three repair plans is taken and simulated side effects that may occur if the actuator means are actuated based on the repair plan are simulated. It is determined whether a significant effect can be eliminated by activating actuator means other than the actuator means selected by the repair plan.

Then, when it is determined that the secondary effect can be removed, the actuator selected by the repair plan is actually operated, the repair is executed, and the secondary effect is operated by activating the other actuator means. Remove it.

As a result, there is no need to simulate side effects based on the other two plans derived in the repair plan, and the repair operation time can be reduced as a whole.

In the above case, if the first selected repair plan simulates side effects and determines that the simulated side effects cannot be removed by activating other actuator means. If so, it may occur if the first repair plan is abandoned and then a second repair plan is taken up and the selected actuator means is activated based on the second repair plan. Simulates no side effects, determines whether simulated side effects can be eliminated by activating actuator means other than the actuator means, and eliminates side effects When possible, a repair operation based on the second repair plan is performed.

As described above, a certain first restoration plan is extracted from a plurality of restoration plans derived in the inference of the restoration plan, a secondary effect in that case is inferred, and the secondary effect can be removed. In such a case, the repair based on the first repair plan is immediately executed.

And if the remediation plan has too much of a side effect, give it up and choose the next remediation plan,
It simulates the side effects in that case.

In such a case, it is preferable to select which repair plan is to be selected first among a plurality of repair plans derived in the inference of the repair plan, for example, in consideration of the cause of the failure obtained in the failure diagnosis.

In the above embodiment, although the number of actuator parameters is small, the repair itself is considerably limited.
Increasing the number of actuator parameters can further increase repair flexibility and potential.

In the specific example described above, if any of the repair work is successful, the state of the device after the success is determined to be a normal state, and therefore, the digital data value given from each sensor is used. It is preferable that the reference value data of each parameter (the reference value shown in FIG. 5) be updated and the parameters be symbolized based on the new reference value data.

Further, in the above specific example, the operation range of each actuator is not particularly described, but the target model storage unit 14
If the operating range data that sets the operating range of the actuator is included in the device-specific feature data stored in the actuator, the actuator can be operated when the output state of the actuator is within the stored operating range. When the output state of the actuator reaches the upper limit or the lower limit of the stored operating range, it is determined that the operation of the actuator is not possible, and can be used for determining whether the repair work is correct or not.

Furthermore, in the above-described specific example, a system that automatically performs self-diagnosis based on a change in sensor output and performs self-repair has been described.However, the image forming apparatus is provided with a setting key for a self-diagnosis mode, and the like. The self-diagnosis and / or the self-repair may be performed only when the self-diagnosis mode setting key is operated.

In the above description of the specific example, a completely autonomous system, that is, a system in which the device itself performs a self-diagnosis of the presence or absence of a failure without any operation by a service person or a user and self-repairs if there is a failure I explained it.
However, in the present invention, the sensor is deleted from the configuration requirements, so that a service person or the like measures data of a functional state at a predetermined location of the image forming apparatus, and the measured data can be input to the apparatus. Accordingly, it is possible to configure an image forming apparatus capable of performing a self-diagnosis that is not autonomous and performing an autonomous restoration based on the self-diagnosis.

In addition, if a system is configured that only selects an actuator for repairing a failure based on the result of the self-diagnosis performed by the device but does not actually operate the actuator but displays the actuator to be operated, the service The image forming apparatus can be provided with a non-autonomous restoration system in which a man only needs to operate the displayed actuator.

Of course, it is also possible to exclude the constituent requirements of the self-repair system and configure an image forming apparatus having only the self-diagnosis system.

That is, according to the present invention, (1) an image forming apparatus having a completely autonomous self-diagnosis and self-repair system, (2) an image forming apparatus having an autonomous self-diagnosis system and a non-autonomous self-repair system, (3) An image forming apparatus having a non-autonomous self-diagnosis system and a non-autonomous self-repair system, (4) An image forming apparatus having a non-autonomous self-diagnosis system and an autonomous self-repair system, or (5) an image having only an autonomous self-diagnosis system The forming device can be configured as required.

Further, in the present invention, when inferring the repair plan, the adjustment range of the actuator may be taken into consideration and only the actually adjustable actuator may be selected.

More specifically, the actuator is, for example,
In the case of the AVR, the lower limit value of the AVR is “0” and the upper limit value is “100”, and the configuration is such that the setting state of the AVR can be detected as an integer value from 0 to 100. In addition, the target model storage unit 14
A lower limit value “0” and an upper limit value “100” of the AVR are set in advance. Therefore, when the AVR is adjusted to a certain state, the adjustment state of the AVR is 0 to 100 corresponding to the adjustment state.
It is grasped as one of the integer value data.

The repair plan unit 15 grasps the adjustment state of the AVR based on integer data of any of 0 to 100 obtained according to the adjustment state of the AVR, and determines whether or not the AVR can be selected as an actuator for failure repair. I do. That is, the lower limit value and the upper limit value of the AVR stored in the target model storage unit 14 are compared with the current adjustment state value, and it is determined whether the AVR can be further operated in the lower limit direction or the upper limit direction. It is done.

Therefore, by adopting such a configuration for each of the plurality of actuators or for any of the actuators, the inference result of the repair plan is a combination of actuator means that can actually operate. It has the advantage that it can be output and a practical restoration plan can be inferred.

The above description is an example of how to set the operation range, and the operation range may be set by another method and compared with the actual actuator state.

In addition, in the repair plan unit 15, not only the set adjustable range of the actuator and the actual adjustment value are compared, but also in the fault diagnosis unit 12, when the fault diagnosis is performed, the set adjustable range of the actuator is set. May be compared with the actual adjustment value, and this may be referred to.

Furthermore, in the image forming apparatus according to the embodiment of the present invention, for example, a manually operated self-diagnosis mode setting key or switch is provided as the self-diagnosis mode setting means, and the self-diagnosis mode setting key or switch is operated. The self-diagnosis or the self-diagnosis and the self-repair described above can be performed only when the above is performed.

The position of the self-diagnosis mode setting key or switch may be any position, but preferably, it is different from the operation keys for normal image formation, for example, the front panel provided in the image forming apparatus is opened. It may be better to install it inside the device that can be operated in the open state.

<Effect of the Invention> According to the present invention, it is determined whether or not a failure has occurred in the image forming apparatus, and when the failure has occurred, the failure symptom, failure cause, and apparatus state are inferred. And
Based on the inference result, a plurality of prestored cases are searched, and a case most suitable for failure repair is detected. Also, the detected case is corrected as necessary. Then, the failure repair processing based on the case is performed. In case-based failure repair processing, since the repair plan is registered in the case in advance, it is not necessary to infer the repair plan, the time until the start of repair processing can be shortened, and failure diagnosis and failure repair can be performed in a short time as a whole. The image forming apparatus can perform the above.

Further, according to the present invention, the cause of the failure is based on the qualitative data common to the image forming apparatus. Therefore, a self-diagnosis and self-repair system capable of handling an unknown failure not explicitly described is provided. The image forming apparatus may have the same.

Further, the self-diagnosis and self-repair system according to the present invention is not applied to a specific image forming apparatus,
The image forming apparatus can be commonly applied to many types of image forming apparatuses, and as a result, an image forming apparatus having an inexpensive self-diagnosis and self-repair system can be provided.

[Brief description of drawings]

FIG. 1 is a block diagram showing a system configuration of an embodiment of the present invention. FIG. 2 is a flow chart showing the operation of the control circuit in FIG. FIG. 3 is a diagram showing a schematic configuration of the present invention when used in a plain paper copying machine. FIG. 4 is a diagram showing a mathematical model of this embodiment. FIG. 5 is a diagram showing reference value data of each parameter necessary for symbolizing each parameter. FIG. 6 and FIG. 7 are diagrams showing development for failure diagnosis on a mathematical model. FIG. 8A, FIG. 8B and FIG. 8C are flowcharts showing the process of the repair work to which the case is applied in the embodiment of the present invention. 9 to 14 are diagrams showing developments for inference of secondary effects on a mathematical model. FIG. 15 is a diagram showing an operation when a repair plan is selected. FIG. 16 is a diagram showing updated reference value data. In the figure, 1a, 1b, 1c: sensor, 6a, 6b, 6c: actuator, 10: system control circuit, 11: digital signal / symbol converter, 12: fault diagnosis unit, 13: fault simulation Unit, 14 ... Target model storage unit, 15 ...
Restoration Planning Department, 16 Symbol / Digital Signal Converter,
17 ... Indicates a case base storage unit, 18 ... Work script storage unit.

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification number Internal reference number FI Technical indication location G03G 15/08 501 501-716H H05B 23/02 302Y 15/16 G03G 15/04 120 G05B 23/02 302 (56) Reference JP 63-106810 (JP, A) JP 63-129405 (JP, A) JP 63-182710 (JP, A) JP 1-274209 (JP, A) JP-A-1-284905 (JP, A) JP-A-2-2406 (JP, A) JP-A-2-42535 (JP, A) JP-A-2-155006 (JP, A) JP-A-3-68002 (JP, A) JP-A 2-162348 (JP, A) JP-A 2-71280 (JP, A) JP-A 61-36780 (JP, A) JP-A 63-225253 (JP, A) Kaihei 2-20879 (JP, A) JP-A-3-27058 (JP, A) JP-A-58 -221856 (JP, A) JP 63-262663 (JP, A) JP 58-72165 (JP, A) JP 64-81617 (JP, A) JP 2-37368 (JP, A) ) JP-A-62-90669 (JP, A) JP-A-1-276175 (JP, A) JP-A-60-20876 (JP, A) JP-A-62-35916 (JP, A) JP-A-2- 235074 (JP, A) JP-A 1-253764 (JP, A) JP-A 1-253763 (JP, A) JP-A 58-94012 (JP, A) JP-B 7-48180 (JP, B2)

Claims (2)

(57) [Claims] 1. A self-diagnosis and self-repair system for an image forming apparatus that embodies image data to generate a visible image, wherein the image forming apparatus is qualitatively represented using a plurality of parameters. Qualitative data, reference value data that specifies a range of qualitative values for predetermined parameters among the parameters, and fault diagnosis knowledge that sets conditions that the parameters should have for the specific parameters among the parameters, Stored target model storage means, a plurality of sensor means for detecting and outputting a functional state at a predetermined location of the image forming apparatus, and comparing the output of each sensor means with reference value data stored in the storage means. Means for converting to qualitative state data by performing a failure stored in the storage means Based on the diagnostic knowledge, it is determined whether or not a failure symptom occurs in the image forming apparatus, and when the failure symptom occurs, the cause of the failure causing the failure symptom is determined based on the parameter status of the qualitative data. The fault diagnosis and simulation means to be inferred and determined based on the above are listed, and the work of the minimum unit is enumerated with a work number.Each work is in rule format and when the antecedent operation is performed when the antecedent operation is performed. , A work script storage means for storing a plurality of work scripts set for each failure cause, which is represented by the content that the consequent part situation is obtained, each of which is represented by the situation of the plurality of parameters. The status of the equipment before the failure repair and the work number of the above work required for the failure repair are described, and multiple things are classified according to the failure symptom and the cause of the failure. A case storage means for storing the case, a case detection means for detecting an applicable case from a plurality of cases stored in the case storage means based on the failure symptom and the failure cause obtained by the failure diagnosis and simulation means, A means for reading from the work script storage means a work script having the same failure cause as the case detected by the detection means, and a work corresponding to the work number described in the detected case is selected from the read work script. Then, the selected work is compared by comparing the parameter status obtained by the failure diagnosis and simulation means, the state of the apparatus before the failure repair described in the detected case, and the antecedent status of the read work. The application discriminating means and the application discriminating means for discriminating whether or not they can be directly applied to the failure repair are selected. When it is determined that the work can be applied as it is, the first repair execution means for executing the work and the application determining means respond to the read work script in response to determining that the selected work cannot be applied as it is. A means for detecting a preceding work to be performed prior to the selected work from among the listed works, the detected preceding work and the selected work in this order,
After the work is executed by the second repair execution means, the first repair execution means or the second repair execution means for executing the work, the work of determining whether or not the result to be achieved by the work is obtained In response to the result judging means and the work result judging means judging that the work is unsuccessful, an additional work for avoiding the cause of the work failure is detected from the works listed in the read work script. And an additional processing means for performing the work, and a third restoration executing means for executing the selected work again after the additional work detected by the additional processing means is executed. Self-diagnosis and self-healing system for forming equipment. 2. The self-diagnosis and self-repair system for an image forming apparatus according to claim 1, wherein the first repair execution means, the second repair execution means, or the third repair execution means executes work. Whether or not the failure repair is successful is diagnosed by the failure diagnosis and simulation means, and when successful,
The image forming apparatus further includes: a successful case creation unit that creates a new case in which a work number related to the success and a state of the apparatus before failure repair are described, and stores the new case in the case storage unit. Self-diagnosis and self-repair system for.