JPH04130470A - Self-diagnostic and self-repairing system for image forming device - Google Patents

Abstract

PURPOSE:To attain rapid self-repair by executing failure repairing work based upon a case corrected by a case contents correcting means when an application deciding means decides that its work can not directly be applied. CONSTITUTION:A case detecting means retrieves plural cases stored in a case storing means 17 and detects the case of a failure state and a failure cause diagnosed by a failure diagnostic means 12. When there are plural cases detected by the case detecting means, which case is to be applied to repairing work with priority is determined by a priority order determining means. When an application deciding means decides that work included in the case to be applied can not directly be applied to the failure repair of a device, correction necessary for the contents of the case to be applied is executed. When the application deciding means decides that the work can not directly be applied, the work of a corrected case is executed. Thus, the rapid self-repair can be executed.

Description

DETAILED DESCRIPTION OF THE INVENTION <Industrial Application Field> The present invention relates to a self-diagnosis and self-repair system for an image forming apparatus. More specifically, the present invention relates to a device or system that can self-diagnose the operational status of an image forming device and self-repair by utilizing artificial intelligence and knowledge engineering, which have been actively researched in recent years.

<Conventional technology> In the development field of precision machinery and industrial machinery, recent efforts have been made to save labor in maintenance work and extend automatic operation.
Artificial Intelligence
CE: Research on expert systems using so-called AI technology is actively being conducted. Some expert systems can self-diagnose whether a device has malfunctioned,
There are also some devices that self-repair any malfunctions that occur.

<Problems to be solved by the invention> However, in the fault diagnosis system using the conventional expert system, (a) the knowledge is not versatile and fault diagnosis for various targets cannot be performed, and (b) unknown faults occur. (c) As the target becomes more complex, the amount of knowledge required for fault diagnosis increases explosively.
Limitations were pointed out, including (d) difficulty in realizing it, and (d) difficulty in acquiring knowledge.

More specifically, conventional automatic adjustment systems and fault diagnosis systems basically operate corresponding actuators based on the outputs of the sensors. In other words, a type of automatic adjustment and failure diagnosis has been performed using a predetermined combination of sensors and actuators. Therefore, basically, each sensor corresponds to a specific actuator, and the relationship between the two is fixed.

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

(2) For the reason mentioned in (1) above, the relationship between sensor parameters and actuator parameters is strongly dependent on the object and lacks versatility. In other words, it cannot be used for various objects.

(3) The relationship between parameters between each sensor or between parameters between each actuator is irrelevant to control.

Therefore, only simple control can be performed based only on the relationship between the corresponding sensor parameters and actuator parameters, and the types of failures that can be handled are limited in advance.

In other words, at the design stage, possible failures are
A failure countermeasure mechanism must be included, and unknown failures cannot be handled.

(4) For the reason (3) above, it is not possible to predict the side effects on the parameters of other actuators that may be caused by manipulating the parameters of any actuator.

There were problems such as.

In this way, in conventional automatic adjustment systems and fault diagnosis systems, predicted failure A is caused by sensor A and actuator A.
The predicted failure B is performed based on the set A of the sensor B and the actuator B, and the predicted failure C is performed based on the set B of the sensor B and the actuator B.
is performed based on the set C of the sensor C and the actuator C, and so on. It was.

The present invention was made against the background of such prior art, and an object of the present invention is to provide a new self-diagnosis and self-repair system for an image forming apparatus that eliminates the drawbacks of the prior art.

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 to generate a visible image, and the present invention is a self-diagnosis and self-repair system for an image forming apparatus that embodies image data and generates a visible image. If a failure occurs, the failure diagnosis means for inferring the failure symptoms, cause of failure, and equipment status, the failure symptoms that may occur in the target equipment, the cause of failure, and the situation at that time. Cases including the state of the device and the work required for repair are stored in the case storage means based on the case storage means stored at least by cause of failure, and the failure symptoms and causes of failure diagnosed by the failure diagnosis means. A case detection means for detecting applicable cases from among the cases, and when there are multiple applicable cases detected by the case detection means, the current state of the device diagnosed by the failure diagnosis means and in each case a priority determining means for determining which of the plurality of detected cases is to be applied preferentially by comparing the state of the device; Applicability determination means for determining whether or not the work included in the cases prioritized by is directly applicable to failure repair of the equipment by comparing the current state of the equipment with the state of the equipment in each case; Based on the fact that the means determines that the work cannot be applied as is, the case content modification means makes necessary modifications to the content of the detected and prioritized case, and the application determination means applies the work as is. The present invention is characterized in that it includes a repair work execution means that executes a failure repair work based on the case modified by the case content correction means when it is determined that the case is not applicable.

Further, the present invention provides a self-diagnosis and self-repair system for the image forming apparatus, in which the display of the status of the apparatus includes:
Including display using multiple parameters, the application determination means are:
When comparing the current state of the device with the state of the device in a case, a specific parameter out of multiple parameters is looked at, and whether or not the work can be applied is determined based on whether the parameter states match or do not match. This is a characteristic feature.

Function> The case storage means stores cases organized at least by cause of failure. For example, cases of failure causes such as ``incorrect halogen lamp settings'', cases of failure causes of ``defective main band WS voltage'', cases of failure causes of ``defective developing bias'', etc. remembered.

The case detection means searches the cases stored in the case storage means and detects cases of failure symptoms and causes of failure diagnosed by the failure diagnosis means.

If there are multiple cases detected by the case detection means, the priority determining means determines which case is to be applied preferentially to the repair work. In determining the priority, the current device state inferred by the fault diagnosis means and the device state in the case are compared, and the case having the device state closest to the current device state is given priority.

More specifically, since the display of the state of the device includes, for example, display using a plurality of parameters, priority is given to cases with a large number of parameters whose states match.

Next, the application determining means determines whether the work included in the applicable case can be applied as is to repairing the failure of the device. For example, even if the case that includes the device state closest to the current state of the device is selected, if the current state of the device and the device state of the example are not completely equal, then
The work prepared for the case may not be directly applicable to repairing equipment failures.

Specifically, the applicability determining means determines whether or not work is applicable by looking at a specific parameter among a plurality of parameters that indicate the status of the device, and determining whether the status of the specific parameter is the current status of the device. If it matches the case, it is determined that it can be applied, and if it does not match, it is determined that it cannot be applied.

If the application determination means determines that the work cannot be applied as is, necessary modifications are made to the content of the case to be applied. Necessary corrections include, for example, inserting a previous work so that another work is performed before the work originally included, or performing the work originally included in 1M to obtain a predetermined state. This means adding and inserting other work if there is no such work.

When the application determining means determines that the work cannot be applied as is, the modified case work is executed.

<Embodiment> Overview of System Configuration FIG. 1 is a block diagram showing the system configuration of an embodiment of the present invention. This system includes multiple sensors la, lb installed on the target machine.

1c and a plurality of actuators 6a, 6b, and 6c for changing the functional state of the target machine.

The plurality of sensors la, Ib, and lc are each for detecting a change in an element of the target machine or a state related to the machine elements caused by the operation of the target machine.

Multiple sensors la, lb.

The information taken in from 1c is amplified by an amplifier circuit 2, converted from an analog signal to a digital signal by an A/D conversion circuit 3, and provided to a system control circuit 10-1.

The system control circuit 10 includes a digital signal/symbol conversion section 11, a fault diagnosis section 12, a fault simulation section]3. It includes a target model storage section 14, a restoration planning section 15, and a symbol/digital signal conversion section 16. Furthermore, the repair planning section 15 is connected to a case base storage section 17 and a work script storage section 18 .

The digital signal/symbol conversion section 11 is for converting the digital signal provided from the A/D conversion circuit 3 into qualitative information. That is, a digital signal, for example, normal.

A conversion function is provided to convert to any of the three symbols, high and low. Sensor la, lb,
1. By converting the signal given by c into such qualitative information symbolized, the approach to fault diagnosis becomes easier. Note that the symbols are not limited to three such as normal, 1-i, and low as in the second example, but may be other expressions such as on and off or A, B, C, and D. Conversion unit] When a digital signal is converted into a symbol in step 1, the target model storage unit 14
Characteristic data specific to the target machine stored in the target machine is referred to. Details of this feature data and signal conversion will be described later.

The failure diagnosis section 12 and the failure simulation section 13 are
By comparing the symbols converted by the digital signal/symbol conversion unit 11 with the fault diagnosis knowledge stored in the target model storage unit 14, the presence or absence of a fault is determined, and fault diagnosis is performed. This is a component that expresses and outputs the failure state of the machine using qualitative information, that is, symbols.

The repair planning unit 15, the case base storage unit 17, and the work script storage unit 18 are components for inferring a repair plan and deriving repair work based on the inference result of failure diagnosis when a failure occurs. . In inferring a repair plan and deriving a repair operation, cases related to past repair successes stored in the case base storage unit 17 are searched, and a work script (for performing a repair operation) for executing the searched successful case is searched. (a series of work units for the process) is selected from the work script storage section 18. In addition, the target model storage unit 1
Qualitative data (described in detail later) stored in 4 is utilized.

Note that the failure diagnosis section 12, the failure simulation section 13,
Repair planning unit 15, case base storage unit 17, and work flow lib] The method of fault diagnosis and failure simulation in the storage unit 18, inference of a repair plan, and derivation of a repair work will be described in detail later.

The repair work output from the repair planning unit 15 is represented by the symbol /
In the digital signal conversion section 16, the stored information in the target model storage section 14 is referred to and converted into a digital signal.

The digital signal is then converted from a digital signal to an analog signal by a D/A conversion circuit 4 and is provided to an actuator control circuit 5. The actuator control circuit 5 controls a plurality of actuators 6a, 6b, based on an applied analog signal, that is, an actuator control command.
5c is selectively activated to perform repair work.

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

Sensor 1. The a, lb, or IC detection signal is amplified, converted into a digital signal, and read into the system control circuit 10, for example, every predetermined read cycle (step Sl).

The read digital signal is converted into symbols in the digital signal/symbol converter 11 (step S2
). This symbolization is performed based on characteristic data preset in the target model storage unit 14, that is, reference value data specific to the target machine. For example, in the target model storage unit 14, the output ranges of the sensors 1a, lb, and lc are set as follows as standard value data for the target machine.

That is, sensor 1a: less than output kaI - low output ka1 to ka2 - exceeds normal output ka2 - high sensor 1b: less than output kbz - low output kb+ - kb2 - exceeds normal output kb2 - high sensor IC: less than output ke1 - low output kc1 In ~, C2 - exceeds normal output kc2 - is set to high. Digital/'symbol converter 11
Now, based on the reference value data specific to the target machine set in the target model storage unit 14, the sensors 1a to I
The digital signal from C is converted into symbols such as ``ro'', ``no n le'', or ``no han i''.

Next, the converted symbol is evaluated in the fault diagnosis unit 12, and the presence or absence of a fault is determined and the fault symptom is specified (step S3). The target model storage unit 1 is used to determine the presence of a failure and identify failure symptoms by weighing symbols.
The fault diagnosis knowledge stored in 4 is utilized. 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, there is a failure,
The failure symptom is determined based on the specific parameter. If there is no failure, step Sl
, S2 and S3 are repeated.

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

Specifically, stored in the target model storage unit 14,
[Based on the qualitative data, the fault diagnosis unit 12 searches for the parameters that are causing the fault, and performs a fault simulation.] In section 13, a fault condition is simulated assuming that the retrieved parameter is the cause of the fault. Sa-・ni,
In the failure diagnosis section 12, the simulation results are compared with the current parameter values, and the validity of the assumption that the retrieved parameter is the cause of the failure is determined. The above processing is performed on a plurality of parameters to be searched.

As a result of fault a non-determination, fault diagnosis, and fault state simulation, the fault symptoms and fault causes of the target machine are determined. Here, the failure symptom is a change in the output status of the target machine (for example, in the case of a copying machine, the copied image is pale), and the failure cause is the cause of the change in the symbol. Changes in the mechanism or structure of the target machine (for example, in the case of a copying machine, a decrease in the light intensity of a halogen lamp).

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

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

If the repair work based on the case is successful, the process ends (YES in step S8), but if the repair work based on the case is not successful (No in step S8), a repair method is inferred. (Step S9), and a simulation of side effects is further performed (Step S9).
10) A repair plan is determined, and repair work based on the determination is executed (step 511).

Although the inference and work execution in steps 59 to Sll are not based on cases, if the repair work based on this inference is successful, the repaired hairstyle is registered in the case base storage unit 17 as a new case. .

Next, methods of fault diagnosis and fault repair will be explained in detail with reference to specific examples. In the following explanation, −
As an example, a method will be described in which the peripheral area of the photoreceptor drum in a small-sized ordinary copying machine is targeted.

FIG. 3 is an illustrative diagram showing a specific target machine. In FIG. 3, 21 is a photosensitive drum, 22 is a main charger, 23 is a halogen lamp for illuminating the original, 24 is a developing device, and 25 is a transfer charger.

In this embodiment, for example, there are three sensors la.

lb and lc are provided. That is, sensor 1a is an AE sensor for measuring the amount of light incident on the photoreceptor drum, sensor 1b is a surface potential sensor for measuring the surface potential of the photoreceptor drum, and sensor 1c is an AE sensor for measuring the density of the image copied on paper. This is a densitometer for determining 1.

Although not shown in FIG. 3, three types of actuators are provided. That is, the main charging volume VRI is used to change the main charging voltage of the photoreceptor drum, and the lamp volume A is used to control the light amount of the halogen lamp.
Three volumes are provided as actuators: VR and a transfer volume VR2 for controlling the transfer voltage between the photosensitive drum and copy paper.

By the way, the target machine shown in Fig. 3 can be viewed from a physical perspective, and the target machine can be expressed at the physical level as a combination of multiple elements, and the behavior and attributes of each element and the connection relationship between each element can be expressed using parameters. When expressed qualitatively using this method, it is as shown in Table 1. The expression format shown in Table 1 will be referred to as a "substantive model."

In addition, the real model is abstracted and expressed as a connection tree of each parameter'! Expression of s4 diagram as ``mathematical model j
I will call it.

Then, by combining the "substantive model" and the "mathematical model",
We will call it the "target model". The "target model" is qualitative data common to image forming apparatuses that is also utilized for failure repair, which will be described later.

(The following is a blank space) Table 1 "Substantive Model" The contents of the substantive model and the mathematical model as qualitative data are stored in the target model storage unit 14.

Further, the target model storage unit 14 stores reference value data, for example, measured at the time of factory shipment, regarding predetermined parameters among the parameters included in the physical model. This reference value data is characteristic data specific to this image forming apparatus.

For example, in this machine, as shown in FIG. 5, parameters XSv, 0. , ■, reference value data specifying the low, normal, and high ranges are stored, respectively.

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

Furthermore, the target model storage unit 14 stores evaluation function knowledge as an example of failure diagnosis knowledge that serves as a standard for determining whether or not the target machine is operating normally, based on the converted symbol. ing.

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

Evaluation function knowledge includes the following knowledge.

Image density O1 - normal, fogging degree O1 - normal, separation performance S, normal here, 0. ．． 0.・If S does not meet the above conditions, the target machine is not operating normally.

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

AE sensor value: Surface potential sensor value V when the halogen lamp is turned off
, - dark potential V. , and their values are, respectively, fogging degree O6·-50 and dark potential V, -700.

Note that the measurement of the degree of fogging O,. and the dark potential V, may be performed manually, or may be performed using the sensor under certain conditions, for example, each time the target machine is turned on, or each time before copying starts. It may be programmed to be automatically measured by In this embodiment, the latter is adopted.

AE sensor la, surface potential sensor 1b and concentration meter IC
Each person X, V, , Ol, Os・, Vl obtained by
are respectively converted into symbols in the digital signal/symbol conversion section 11.

The conversion is performed for each sensor la, lb or I as described above.
The digital value given from C is stored in the target model storage unit 1.
This is done by comparing it with reference value data as feature data stored in 4, and converting it into one of three types of symbols: normal, high, or low.

In this example, each parameter is symbolized as follows.

X-High ■, -Low 0, -Low ■, -Normal In the fault diagnosis section 12, these symbolized parameters are stored in the target model storage section 14 (which functions as an example of stored fault diagnosis knowledge). It is compared with the evaluation knowledge. As a result, since the image density O1 is not normal, it is determined that there is a failure, and the failure symptom is "Image density is too low (
0. − low)”. And then,
Using "0.-口-" as a failure symptom, a failure diagnosis, that is, a cause of the failure is inferred.

Fault diagnosis is first performed in the fault simulation section 13 using the mathematical model shown in FIG. 4 to search for parameters that may cause 0, -low.

In the mathematical model shown in FIG. 4, parameters that may cause a decrease in Ol are shown in FIG. 6. In FIG. 6, the parameters marked with upward or downward arrows are those that may cause parameter O1-low, and those with upward arrows indicate that the parameter increases, while those with downward arrows indicate parameters that may cause parameter O1-low. If that parameter drops, it causes an 01-low.

Next, each parameter,ζ,D,that can cause,O,-rho was explored in the mathematical model.

V,,7o+Vh,V,,V,,X,β,HL.

Regarding D, the failure diagnosis unit 12 detects the cause of the parameter change.

This detection is performed based on the entity model shown in Table 1, and in this embodiment, the following failure cause candidates are inferred.

That is, (1) - Defective low-nee transfer transformer ζ - Low-lowny paper deterioration ■, - High: → Defective developing bias γ. - Deterioration of low knee toner ■, = Defective low knee raw charging voltage HL - Defective high knee halogen lamp setting D - Low:
→If the original is thin■+-low, the transfer transformer is defective, and ζ-a
The knowledge that - indicates paper deterioration, ■, - high indicates defective developing bias, etc. is knowledge of the cause of the failure, and this knowledge is included in the qualitative data common to image forming devices. ing.

Note that among the parameters, β is the sensitivity of the photoreceptor and is excluded because it does not increase.

D, , V, and X are also excluded since they are represented by other parameters.

Then, in response to the above inference made in the fault diagnosis section 12, a fault state simulation is performed in the fault simulation section 13.

Simulating a fault state means inferring the state of the target machine when the inferred fault occurs. More specifically, it is assumed that the cause of 0-low is the cause of failure, such as a defective transfer transformer, and V1=low is set for the model in the normal state. Then, the influence on each parameter in that state is examined using a mathematical model. - If set to low, 0. -low and S, -low, and all other parameters are normal, so this contradicts the X-high and V,-low obtained from the sensor. Therefore, the result is that the inference regarding the cause of the failure is incorrect.

Similarly, ζ-rho is set on the normal state mathematical model and the result is compared with the symbols obtained from the sensor. In this case as well, on the mathematical model, for X-normal,
Since the symbol from the sensor is X-high, there is a contradiction and the inference of the cause of the failure is determined to be incorrect.

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

As a result, in this example, when the cause of the failure is determined to be "incorrect halogen lamp settings (HL-high)", a result that matches the actual condition of the target machine can be obtained, and other causes of the failure can be determined. It is concluded that all candidates are inconsistent with the actual state of the device.

Therefore, it can be determined that the cause of the failure in this case is improper setting of the halogen lamp. Table 2 shows the status of each parameter of the target machine at that time.

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

Execution of repair work Next, the failure diagnosis section 12 and the failure simulation section 1
Based on the results of the failure diagnosis performed in step 3, repair work is performed according to the flowcharts shown in FIGS. 8A, 8B, and 8C.

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

Note that the flowcharts in FIGS. 8A, 8B, and 8C are based on step S in the flowchart in FIG.
5. S6. This corresponds to S7 and S8, and specifically represents the contents of the repair process in detail.

Case search: According to the above-mentioned fault diagnosis method, the cause of the fault causing the occurring fault symptom is inferred (step 521).
. Based on the results, a large number of cases stored in the case base storage unit 17 (see FIG. 1) are searched, and cases that can be used for repair are detected from among them (step 5).
22).

More specifically, as shown in Table 3, all the cases stored in the case base storage unit 17 include the case number, the parameter status before restoration, the parameter status after restoration,
Failure symptoms, causes of failure, repair work, number of successful applications, and number of failed applications are recorded.

Table 3 Also, each case is hierarchically classified according to failure symptoms and failure causes.

Therefore, the repair planning unit] 5 determines the failure symptoms diagnosed by the failure diagnosis unit 12 and the failure simulation unit 13.
"Image density is too low (05-low)" and cause of failure "
Using "Poor setting of halogen lamp (Ht-high)" as an index, a case that satisfies both the failure symptom and the failure cause is detected. Therefore, even if the failure symptoms are
Even if the image density is low, the cause of the failure may be, for example,
Cases of "defective main charging voltage" are not detected.

As mentioned above, "failure symptoms" here refer to phenomena that are recognized as malfunctions of the target machine, such as "low image density" or "image fog," and "failure causes." indicates a change in the function or structure of the target machine, such as "incorrect halogen lamp settings" or "incorrect main charging voltage."

Now, the failure symptom "Image density is too low" and the failure cause "
As a result of searching for cases by ``Halogen lamp setting failure'', cases (1) to (3) shown in Tables 4 to 6 below were found.
Suppose that is detected.

(Leaving space below) Table 4 Table 5 Table 6 By the way, there are multiple detected cases, three in this case, so it is necessary to decide which case to apply to the repair work first.

Therefore, priorities are assigned to the three detected cases (1) to (3) (steps S23 to S525). The priority regarding the application order is determined by comparing the parameter status before repair in each case with the current parameter status of the target machine simulated in the failure diagnosis (see Table 2) (step 523), and Priority is given to cases with the largest number of matching parameters.

Specifically, when comparing the status of the parameters before each restoration in cases (1) to (3) with the current status of the parameters (Table 2), only the status of v7 is different in case (1). In case (2), the states of v, and V are different. In case (3), the states of vfi, ■b, and Vl are different.

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

Note that if the number of matches between the parameter status before restoration and the current parameter status is equal, the number of successful applications is taken into consideration (step 524), and the higher the number of successful applications, the higher the priority is given.

Furthermore, if the number of matches between the parameter status before repair and the current parameter status is equal, and the number of successful applications is also the same, the number of failed applications is taken into account (step 524), and the smaller the number of failed applications, the higher the priority. A ranking will be given.

Note that, of course, if only one case is detected in step S22, the prioritization regarding the application order is omitted.

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

In executing the repair plan, first, case (1) given the first priority is set in the work register, and based on case (1), the work stored in the work script storage unit 18 is executed. The work script with the failure cause "incorrect halogen lamp settings" is selected from the scripts and set in the work register (step 526).
.

An example of a work script for incorrect halogen lamp settings is shown in Part 7.
Shown in the table.

Table 7 As shown in Table 7, the work script includes the failure cause "incorrect halogen lamp setting" as an index, and multiple tasks 1. 2. 3. ...or has been restored. Each task is described in a rule format and consists of an antecedent situation, an antecedent operation, and a consequent situation. Each task means that when an antecedent part is operated in an antecedent part situation, a consequent part situation is obtained.

To be more specific, for example, in the case of work 1, the antecedent state is the parameter HL-high, and in this state, by performing the antecedent operation of decreasing the lamp volume AVR, the parameter H
, -Normal, that is, the consequent situation is obtained.

Note that the work script is set for each cause of failure and lists the minimum unit of work.

Work scripts are set for each cause of failure, so
There are as many work scripts as there are causes of failure.

When case (1) and the work script in Table 7 are set in the work register (step 526), next, the restoration planning unit 15 determines the status of the parameters before restoration of case (1) set in the register. It is checked whether it completely matches the current parameter situation (step 52).
7).

If the parameter situation before repair in case (1) completely matches the current parameter situation, the work with the number written in the repair work column of case (1) will be changed to “Halogen "Improper lamp setting" work script is selected and executed (step S2g) (in reality, the case (
Since the parameter status in 1) does not completely match the current parameter status shown in Table 2, the actual processing is as follows.
As will be described later, the process will proceed to steps 527-S34).

If the consequent status is obtained as a result of executing the task, it is determined that the task is successful (YES in step S29), and it is further determined whether there is a next task (step 530). . If there is a next work number in the repair work column, that work is selected from the work script and executed (step 528), and it is determined whether the work is successful or not (step 529), and the process is repeated.

Then, if there is no more work to do (step 53),
(NO if 0), the value in the column of the number of successful applications in the case is 1
The number of successes is increased and the number of successes is registered (step 531).

Even if the task is executed, if the consequent status of the task is not obtained, it is determined that the task has failed (No in step S29), and the value in the application failure number column is incremented by 1 and the number of failures is registered. is performed (step 532).

Then, it is determined whether there is a case with the next priority (
Step 833), in some cases (Y in step S33);
ES), for the next priority case, step S2
Processing from step 6 is performed.

If there is no case with the next priority (No in step S33), a repair plan is inferred taking into consideration the side effects that will be detailed later, that is, OMS processing is performed (step S53).
4).

Then, it is determined whether the 0MS processing was successful or not (step 535), and if it is determined that the 0MS processing was successful (YES in step S35), a new case is created based on the data obtained by the 0MS processing. and the case is registered in the case base storage unit 17 (step 53).
6). Then, the process ends.

If the 0MS process ends in failure (step S35
(NO), no new case is registered and the process ends.

Now, as mentioned above, the parameter status before repair in case (1) does not completely match the current parameter status, and the status of the parameter V, , is inconsistent.
The actual process is determined to be NO in step S27, and the eighth
The process advances to step S37 shown in Figure B.

In step S37, the work numbered in the repair work column of case (1) is designated from the "incorrect halogen lamp setting" work script. That is, work 1 is designated. Then, the antecedent status of work 1 and the current parameter status are compared, and it is determined whether they match or do not match (step 538).

When performing a task, the current parameter situation or the antecedent situation of the task must match. In this specific example, both the antecedent status of work 1 and the current parameter status are parameter HL-high, so the antecedent status matches the current parameter status. If they match (YES in step 53g),
The work 1 is executed (step 539). and,
The success or failure of the task is determined (step 540), and if the consequent status is obtained as a result of the task execution, it is determined that the task was 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 entered in the repair work column of case (1) (step 541), and if there is a next work (
YES in step S41), the next work is specified, the antecedent status of the work and the current parameter status are compared (step 537), and the same as described above is performed in step S38.
The subsequent processing is repeated.

If there is no more work to do next (step S4), select NO.
), flag A or B (the purpose of these flags A and B will be described later) is determined (step 542), and both flags A and B are in the state.
If it has not been set or set (NO in step S42)
, the numerical value in the column of the number of successful applications of case (1) is incremented by 1, the number of successful applications is registered (step 343), and the process ends.

However, in step S39, even if the ramp volume AVR, which is the antecedent part operation, is decreased, the parameter HL does not change to normal as a result, and even if the ramp volume AVR is decreased to its lower limit, the consequent part status still changes. If the parameter HL-normal is not obtained, it is determined that the operation has failed (No in step S40).

In other words, the parameter state (
If the current parameter state after the work does not become the parameter state (consequent state) set in the repair work, the work is determined to have failed.

At this time, a second additional process is executed to avoid the cause of work failure, which will be explained below along the flow of FIG. 8C.

That is, first, the failure symptom "Image density is too low (0.
- Low)" and the failure cause is "Improper setting of the gen lamp (HL-High)". Among these cases, in the repair work column, the work number that was determined to be a failure, for example, work 1, is searched. All instances in which the number is listed are detected (step 549). Then, the parameter status before repair and the current parameter status of all detected cases are compared (step 550), and the differences between the parameter status before repair and the current parameter status are determined.
Parameters that are common in all cases are detected. In other words, a common feature of the parameter situations before restoration of all cases and a difference between the current parameter situations are detected (step 551).

In the specific example, the cases in which work 1 is listed in the repair work column are cases (1) and (2). The parameter situations are compared, and the parameter ■. is taken as a common difference.In other words, in the parameter situation before repair, the parameter V, -low is common, but in the current parameter situation, the parameter V7-normal.

Then, if it is determined that there is a different parameter (YES in step 551), that parameter, that is, parameter v6-normal in the specific example, is hypothesized to be the cause of the failure of this work, and this parameter V1 is changed from normal to normal. A work that can be performed is searched for in the work script (step 552), and its presence or absence is determined (step 853).

Looking at the work script in Table 7, it can be seen that the parameter V, , can be changed from normal to low by work 5, so it is determined that there is work (YES in step S53).

In this case, the work interval in case (1) is temporarily corrected, and work 5 is inserted. Also, to indicate that this temporary correction has been made, flag B is set (step 55).
4). Operation 5 is then performed (step 555).

If the result of execution of work 5 is ■7-low, it is determined that the work is successful (YES in step S56).
).

In this case, ■, , -low are essential conditions as the antecedent status of work 1. Therefore, the antecedent status of work 1 in the work script shown in Table 7 is corrected to add V,, -row, and the work script in Table 7 is rewritten as shown in Table 8 (step 557 ).

In the work script shown in Table 8, the antecedent status of work 1 is "HL-high and -low".

(The following is a margin) Table 8 Then, the processing from step S37 shown in FIG. 8B is performed again. In this specific example, if work 1 is performed and is successful (YES in step S40), there is no further work to be performed (NO in step S41).
), it is determined that flag B is set (
YES in step S42). Therefore, based on the parameter situation and processing at this time, a new case (1-1
) is created and registered. Additionally, flags A and B are reset (step 544). This new case is shown in Table 9.

(Here are blank spaces) Table 9 The difference between case (1-1) shown in Table 9 and case (1) shown in Table 4 is that the parameter V7 is normal in the parameter situation before repair. And in the repair work column, r5. The point is that two operations called IJ are described. Note that the number of successful applications of case (1-1) is only one time, and the number of failed applications is 0.

Now, in step S56 of FIG. 8C, if it is determined that the work was not successful, the parameter V is also set.

It is determined whether there is a task in the work script that can change the value from normal to low (step 558).
, if there is work, the processing from step S54 is performed.

Furthermore, if it is determined in step 551 that there are no different parameters, or if it is determined that there is no work in step 553, flags A and B are reset (step 559), and then the other cases, i.e. It is determined whether there is a next application priority case (step 560).

Then, if there is the next case (Y in step S60)
ES), the case and the corresponding work script are set in the work register (step 561), and the processing from step S37 shown in FIG. 8B is performed.

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

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

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

Next, the "1" displayed in the case (2) repair work column specifies work 1 of the work script, and its antecedent status and current parameter status are compared (step 5).
37), it is determined whether the two match or do not match (step 538). As is clear from the comparison between Table 7 and Table 2, the antecedent status of Work 1 and the current parameter status match at HL-High, so Work 1 is executed (Step 539).

When the state becomes Ht-normal, it is determined that the task has been successful (YES in step 540), and it is determined whether there is a next task (step 541).

In case (2), since work 2 exists as the next work, the process proceeds to step S37.about., the next work 2 is specified, and its antecedent status and current parameter status are compared. As a result, it is determined that the antecedent state V, -low of work 2 does not match the current parameter state V-normal (No in step S38).

As described above, when executing a task, the current parameter situation must match the antecedent situation of the task. Therefore, a first additional process is performed. That is,
A search is made to see if there is another task in the task script shown in Table 7 that allows the current parameter situation to match the antecedent situation (step 545).

Looking at Table 7, it can be seen that work 4 brings the parameter V to low when HL-normal. Therefore, YES is determined in step S46, and the process proceeds to step S47. Then, the repair work column of case (2) is provisionally corrected to rl, 4, 2J, and flag A is set to indicate that the provisional correction has been made (step 547).

Next, work 4 added as a result of the temporary correction is executed (step 548), and it is determined whether or not work 4 is successful (step 540).

If the execution of this task 4 is successful (step 340
YES), the presence or absence of the next task is determined (step 5).
41). In case (2), since work 2 exists as the next work, the process proceeds to step S37 again, the next work 2 is designated, and its antecedent status and current parameter status are compared. As a result, the current parameter situation is
By executing work 4 in step S48 above, ■
- Since it is low, it matches the antecedent situation of work 2.

Therefore, it is determined YES in step S38, and work 2 is performed.
is executed (step 539).

Then, it is determined whether or not the execution of work 2 was successful (step 540), and if it was successful, in step 541,
It is determined whether there is a next task.

In case (2), there is no next task, so the process advances to step S42. Then, it is determined that flag A is set (YES in step S42), and the case in which the repair work column is temporarily corrected in step S47 is registered as a new event f11 (2-1) (step 544)
. Additionally, flags A and B are reset (step 544).

This additionally registered case (2-1) is shown in Table 10.

The case (2-1) shown in Table 10 is the case (2) shown in Table 5.
The difference is that the parameter V is -normal in the situation before restoration, and the restoration work is rl, 4.2J. Further, the number of successful applications is 1 this time, and the number of failed applications is O.

(Margins below) Table 10 Note that if it is determined in step S46 that there is no other work that can match the current parameter situation with the antecedent part situation (No in step S46), the process continues in step S46. The process advances to step S59 shown in FIG. 8C.

When carrying out repair work, employing the method of searching for cases and applying them as described above is particularly effective for devices such as small ordinary copying machines as explained in the above specific example.

This is because devices such as small-sized ordinary copying machines have unstable elements (for example, active use of chemical changes, etc.) as control targets in their constituent systems. Therefore, due to changes in the conditions in which the component system is placed, such as environmental changes or structural deterioration, the relationship between the sensor parameters and the actuator parameters may change. Searching for cases in the explanation of the above specific example can be said to be because the device collects changes between these parameters while running, performs a type of learning using that information, and tunes its intelligence. Even if a change occurs between the above parameters, repair work can be performed to effectively deal with the change.

In other words, when the relationship between the parameters of the target machine changes, the example is modified and a new example is created based on the change, and the contents of the work script are also modified.

In the above explanation, the example and the work script are stored separately, and are selected and set separately.

However, instead of this example, for example, if the repair work column for each case is stored with the work itself rather than the work number in the work script, the specific work to be performed can be stored. This can be an example of

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

Inference of repair plan Next, inference of repair plan, that is, step S5 in FIG.
The QMS processing shown in 1 will be explained.

As a result of fault determination, "Image density is too low (0°-low)"
” was taken up as a failure symptom, the goal of repair was to
The goal is to increase Ol.

Therefore, from the relationship in the mathematical model shown in Figure 4, it is inferred that Ol, which is the repair target, can be increased by increasing D, 1, or ζ. be done.

Next, when inference is made with the goal of increasing D, ■
Raise , or lower ■, or γ.

Either rise or get a conclusion. in this way,
By repeating the inference based on the mathematical model,
Candidates for repair operations can be obtained on the mathematical model. The results obtained are shown in Table 11.

(Margins below) Table 11 By the way, among the repair candidates obtained based on the mathematical model, there are some that can be realized and some that cannot be realized. For example, D: The optical density of the original cannot be changed, and β: The sensitivity of the photoreceptor is also difficult to change.

γ0 Nitoner sensitivity cannot be changed, Sensitivity of ζ2 paper is also unchangeable.

Also, in this specific example, V,: bias voltage It is also impossible to change because there is no actuator.

Of course, by adding an actuator, V,
can be made changeable.

In addition, Therefore, it is excluded from the repair candidates here.

Although not directly related to this specific example, the amplitudes of the A and p separation AC voltages can also be changed by adding an actuator.

According to the above, in this specific example, vl : transfer voltage ■, : surface potential after main charging HL : logarithm of halogen lamp output light quantity are taken up as repair candidates.

On the other hand, the target model storage unit 14 stores in advance the following knowledge as repair plan knowledge. That is, (a) Increase V, 1. Increase the control voltage of the transfer transformer. (b) Decrease V, 1. Decrease the control voltage of the transfer transformer. cc) Increase V, 1. Increase the control voltage of the lifetime charging transformer. (d)V. Decrease the control voltage of the lifetime charging transformer (e) Increase Ht - shift the halogen lamp control signal to the high voltage side (f) Decrease HL - shift the halogen lamp control signal tz to the low voltage side .

It is. The repair plan knowledge stored in the target model storage unit 14 is characteristic data specific to this device. By applying the repair plan intelligence to the repair candidates obtained based on the mathematical model, 0. As a repair operation to increase V, (a) Increase the control voltage of the transfer transformer to increase V, (C) Increase the control voltage of the charging transformer to increase v, (f) Decrease HL - Halogen Three methods are available for shifting the lamp control signal to lower voltages.

If you simply want to increase the image density O1, these three
Repair is possible by performing any of the methods.

However, the target machine may be affected by various side effects by increasing the image density O8. Therefore, in this embodiment, as explained below, the inference of secondary effects is performed based on a mathematical model.

When the three repair plans derived in the inference of the side effect repair plan are developed on a mathematical model, FIGS. 9 to 14 are obtained. In other words, (a) Figures 9 and 10 show the case where V is increased (Figure 10 shows O5 when D-〇 is expressed on the mathematical model), (c) V , in the case of raising the
05 is expressed on the mathematical model),
(f) When HL is lowered, the results are as shown in FIGS. 13 and 14 (in FIG. 14, the OS in the case of D-0 is represented on a mathematical model).

Then, when a functional evaluation is performed based on the mathematical model, the following state is inferred.

That is, (1) When V is increased (Figures 9 and 10) (a
) Output image density increases.

<b) When D-0, 0. 〉It may be normal. In other words, fogging may occur.

(c) S>normal, which may result in poor separation.

(2) When V is increased (Figures 11 and 12) (a) Output image density increases.

(b) When D-0, 0.・〉It becomes normal and fogging may occur.

(3) When HL is lowered (FIGS. 13 and 14) (a) Only the output image density increases, and there are no other side effects.

Therefore, the repair planning unit 15 selects a repair plan that has the least side effects, that is, a repair plan that lowers the HL. This repair plan is consistent with the operation to eliminate the cause of the failure determined by the failure diagnosis.

In other words, looking at it from a different perspective, inferring the cause of a failure in fault diagnosis involves tracing the actual state of the device when it fails on a mathematical model and understanding the state of each component when the device fails. In contrast to the previous method of estimating the cause of a failure, estimating a repair plan is based on the assumption that the device is not in a faulty state, but rather that the device is normal, tracing the state of the device on a mathematical model, and performing repairs based on that. Reasoning plans.

In the above-mentioned specific example, the same failure cause and repair plan were obtained both in the inference in the failure diagnosis and in the inference in the repair plan.

However, in some cases, the results obtained by the two methods may differ because inferences for fault diagnosis are based on devices in a faulty state, while inferences for repair plans are based on devices in a normal state. .

In such a case, if only those conclusions that do not contradict the conclusions obtained in the inference process of failure diagnosis are selected when inferring a repair plan, the inference process of the repair plan can be performed in a shorter time.

In the above case, if the repair plan of lowering the HL cannot be selected, for example if the AVR volume for shifting the halogen lamp control signal to the lower voltage side is already at the lower limit, then the secondary V of (2) has little influence. A restoration plan is selected to increase the

However, ■. If a repair plan that increases Oa is selected, it is predicted that there will be a side effect of the possibility of fogging, so in the mathematical model shown in Figure 12, which parameter should be selected to lower Oa? The appropriate operation is examined based on the mathematical model shown in FIG. 12, and the operation is selected based on the knowledge of the repair plan. As a result, it is selected whether to raise HL, lower ①, or lower vl, and a repair plan including prevention of fogging is carried out.

That is, as shown in FIG. 15, the reasoning for the repair operation is developed assuming side effects. In the inference development of repair operations as shown in Figure 15, (a) techniques that contradict the previous repair plan on the mathematical model are not selected (b) techniques with the least side effects are selected (C ) The expansion is based on the knowledge that the loop that forms will stop expanding at that point.

In Figure 15, after all, (1) V, ↑-H5↑-v7↑ loop, and (2) V, ↑→V, ↓-V. Two repair plans remain, the loop above.

Now, suppose that when the loop (1) is performed as a restoration plan, the image density becomes an appropriate density, that is, Ol becomes normal. In such a case, since Vo and H have been increased, the value of the surface potential measured by the sensor 1b in the pre-repair state where the image density O1 has returned to normal is considerably greater than the value initially measured. It should have changed to something higher. However, since the repair operation was successful, the state of the parameter V after repair must be symbolized normally. Therefore, in such a case, when the repair is completed, the reference data for symbolizing the parameter V shown in FIG. 5 is changed based on the measured value measured by the sensor 1b,
For example, the data is rewritten to the data shown in FIG.

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

In this embodiment, the above-mentioned (1) in FIG.
Specifically, when the main charging volume VRI is operated to increase the surface potential of the photoreceptor drum 21, and if a rash occurs on the resulting copy, the lamp volume AVR is operated to increase the halogen charge voltage. The light intensity of the lamp is increased and the image density of the copy is diluted.

Then, while increasing the main charging volume VRI and the lamp volume AVR alternately and appropriately, when the image density becomes normal, that is, when the parameter O becomes normal, it is obtained from the detection output of the densitometer that is the sensor 1c. When this occurs, the repair process is terminated.

Furthermore, if the above two repair plans are infeasible, a repair plan of increasing V in (3) above is selected, resulting in the secondary effects of fogging and poor separation. A fault diagnosis is performed based on one assumption, and a repair plan is selected.

Then, the selected repair plan is executed, and in the case of loop processing, it is determined that the plan has failed when the operation of the parameters on the loop reaches its limit.

In addition, in the case of this embodiment, as explained in the specific example, the end of repair is determined when Ol becomes normal,
In this state, the repair is stopped.

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

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

That is, suppose that, for example, three repair plans are derived in the repair plan inference. In that case, only one of the three repair plans is selected, and the side effects that may occur if the actuator means is actuated based on the repair plan are simulated, and the simulated side effects are It is determined whether the subsequent effects can be removed by actuating actuator means other than the actuator means selected by the repair plan.

When it is determined that the side effects can be eliminated, the actuators selected by the repair plan are actually operated to carry out the repair, and the side effects are eliminated by activating other actuator means. It removes it.

As a result, there is no need to simulate the side effects based on the other two plans derived from the repair plan, and overall the repair operation time can be shortened.

In the above case, also (7) for the first selected repair plan, a side effect is simulated and the simulated side effect cannot be removed by actuating other actuator means. This occurs when the first repair plan is abandoned, the second repair plan is taken up, and the actuator means selected based on the second repair plan is operated. determine whether the simulated side effect can be eliminated by actuating an actuator means other than that actuator means; If it can be removed, repair work based on the second repair plan is performed.

In this way, if the first repair plan is extracted from among the multiple repair plans derived in the inference of the repair plan, the side effects in that case are inferred, and the side effects can be removed. Then, immediately execute the repair based on the first repair plan.

If that repair plan has too many side effects, it is abandoned, the next repair plan is selected, and the side effects in that case are simulated.

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

In the above embodiments, the repair itself is severely limited due to the small number of actuator parameters; however, by increasing the number of actuator parameters, the flexibility and possibility of repair can be further improved.

In the specific example explained above, if any repair work is successful, the state of the equipment after the success is determined to be normal, so it is determined based on the digital data values provided from each sensor. It is preferable that the reference value data of each parameter (the reference values shown in FIG. 5) be updated and the parameters be symbolized based on the new reference value data.

Further, in the above-mentioned specific example, although the operating range of each actuator was not particularly mentioned, the target model storage unit 14
If the operating range data that sets the actuator's operating range is included in the characteristic data specific to the device stored in the device, 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 or lower limit of the stored operating range, it can be determined that the actuator cannot be operated, and this can be used to determine whether the repair work is correct or not.

Furthermore, in the above-mentioned specific example, a system that automatically performs self-diagnosis and self-repair based on changes in sensor output was introduced, but if the image forming apparatus is provided with a self-diagnosis mode setting key, etc. Self-diagnosis and/or self-repair may be performed only when the self-diagnosis mode setting key is operated.

In the explanation of the specific example above, the device is completely autonomous, that is, without any operation by service personnel or users (the device itself automatically performs self-diagnosis to determine if a failure occurs, and if a failure occurs, it self-repairs). However, in this invention, the sensor is removed from the configuration requirements, and a service person or the like measures functional status data at predetermined locations of the image forming apparatus, and the measured data is transferred to the apparatus. By having a configuration that allows input, it is possible to configure an image forming apparatus that can perform non-autonomous self-diagnosis and perform autonomous repair based on the self-diagnosis.

In addition, if you configure a system that only selects the actuator to repair a failure based on the results of the self-diagnosis performed by the device, but displays the actuator that should be operated without actually operating the actuator, service The image forming apparatus may include a non-autonomous repair system, in which a person only needs to operate the displayed actuator.

Of course, it is also possible to exclude the configuration 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 fully 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) (4) an image forming device having a non-autonomous self-diagnosis system and a non-autonomous self-healing system; (4) an image forming device having a non-autonomous self-diagnosis system and an autonomous self-healing system; or (5) an image forming device having only an autonomous self-diagnosis system. The forming device can be configured as desired.

In addition, in this invention, when inferring a repair plan,
In consideration of the adjustment range of the actuator, only actuators that are actually adjustable may be selected.

To explain more specifically, the actuator is, for example, A
In the case of VR, the lower limit value of AVH is "0" and the upper limit value is rlo
oJ, and the configuration is such that the AVH setting state can be detected as an integer value from 0 to 100. In addition, the lower limit value “0” and the upper limit value r1 of the AVR are stored in the target model storage unit 14.
Set 00J. Therefore, when the AVR is in an adjusted state, the adjusted state of the AVH is understood as integer value data of any one of 0 to 100 corresponding to the adjusted state.

The repair planning unit 15 uses integer value data from 0 to 100 obtained depending on the adjustment state of the AVH
It is determined whether the AVR can be selected as the actuator for troubleshooting. In other words, the lower limit and upper limit of AVH 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 toward the lower limit or toward the upper limit. It will be done.

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

Note that the above explanation is just an example of how to set the operating range, and the operating range may be set using another method and compared with the actual state of the actuator.

In addition, the repair planning section 15 not only compares the set adjustable range of the actuator with the actual adjustment value, but also the failure diagnosis section 12 when performing failure diagnosis.
The set adjustable range of the actuator and the actual adjustment value may be compared and 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 above-mentioned self-diagnosis or self-diagnosis and self-repair can be performed only when the

The location of the self-diagnosis mode setting key or switch is as follows.
Although it may be placed in any desired location, it is preferable to provide it in a location different from the operation keys for normal image formation, such as inside the image forming device so that it can be operated with the front panel open. It would be good.

<Effects of the Invention> According to the present invention, it is possible to determine whether or not a failure has occurred in the image forming apparatus, and when a failure has occurred, the symptoms of the failure,
The cause of the failure and the condition of the equipment are inferred. Then, based on the inference result, a plurality of pre-stored cases are searched, and the most suitable case for fault repair is detected. Also,
Detected cases are corrected as necessary. Then, failure repair processing is performed based on the case. In case-based fault repair processing, the repair plan is registered in the case in advance, so there is no need to reason about the repair plan, which shortens the time it takes to start repair processing, and overall the fault diagnosis and fault repair can be done in a short time. The image forming apparatus can perform the following steps.

Further, according to the present invention, the cause of failure is determined based on qualitative data common to image forming apparatuses, and the self-diagnosis and self-repair system can also handle unknown failures that are not explicitly described. The image forming apparatus can have the following.

Furthermore, the self-diagnosis and self-repair system according to the present invention can be commonly applied to many types of image forming apparatuses, rather than to a specific image forming apparatus, and as a result, is inexpensive. An imaging device having a self-diagnosis and self-repair system can be provided.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the system configuration of an embodiment of the present invention. FIG. 2 is a flowchart 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 representing a mathematical model of this embodiment. FIG. 5 is a diagram showing reference value data of each parameter necessary when each parameter is symbolized. FIGS. 6 and 7 are diagrams showing development for diagnosis of late II on a mathematical model. FIGS. 8A, 8B, and 8C are flowcharts showing repair work processing to which an example of an embodiment of the present invention is applied. FIGS. 9 to 14 are diagrams showing development for inferring side effects on a mathematical model. FIG. 15 is a diagram showing operations when selecting a repair plan. 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 conversion section, 12...Fault diagnosis section, 13...Failure simulation section, 14...Target model storage Part, 15...Repair planning part, 16... Symbol/digital signal conversion part,
17 shows a case base storage unit, 18 shows a work script storage unit.

Claims (1)

[Scope of Claims] 1. A self-diagnosis and self-repair system for an image forming device that embodies image data to generate a visible image, which system is capable of determining whether or not the target device is malfunctioning. Fault diagnosis means for determining and inferring the failure symptoms, cause of failure, and equipment status when a failure occurs, failure symptoms that may occur in the target equipment, cause of failure, status of the equipment at that time, and repair. Examples include the work required for
Case storage means stored at least by cause of failure; case detection means for detecting applicable cases from among the cases stored in the case storage means based on the failure symptoms and causes of failure diagnosed by the failure diagnosis means; , when there are multiple applicable cases detected by the case detection means, the detected cases are detected by comparing the current state of the device diagnosed by the fault diagnosis means with the state of the device in each case. A priority determination means for determining which of the cases to be applied preferentially, and a work included in a case detected by the case detection means and given a priority by the priority determination means is directly executed on the device. Application determining means determines whether or not the work is applicable to failure repair by comparing the current state of the equipment with the state of the equipment in each case. Based on the fact that the application determining means determines that the work cannot be applied as is, case content modification means for making necessary modifications to the content of the detected and prioritized case, and when the application determination means determines that the work cannot be applied as is, after the content has been modified by the case content modification means; A self-diagnosis and self-repair system for an image forming apparatus, comprising: a repair work execution unit that performs a failure repair work based on a case of the above. 2. In the self-diagnosis and self-repair system for an image forming apparatus according to claim 1, the display of the state of the apparatus includes a display based on a plurality of parameters, and the application determining means is configured to determine the current state of the apparatus and the case. When comparing the state of the device, attention is paid to a specific parameter among a plurality of parameters, and whether or not the work can be applied is determined based on whether the parameter states match or do not match.