JPH04130469A - Self-diagnostic and self-repairing system for image forming device - Google Patents

Abstract

PURPOSE:To attain rapid self-repair by executing work included in a case detected by a case detecting means taking the priority order of application through a priority order determining means as failure repairing work, and after completing the repairing work, additionally storing the current case of a device in a case storing means as a new case. CONSTITUTION:The case detecting means retrieves plural cases stored in the case storing means 12 and detects the case of a failure state and a failure cause diagnosed by a failure diagnostic means 12. Then work included in the case detected by the case detecting means and taking the priority order through the priority order determining means is executed. After completing the repairing work, whether the failure has been removed or not is decided, and when the failure has been removed, the success of repair is decided, the case including the current failure state and failure cause of the device, the state of the device and the executed work is additionally stored as a new case and the number of cases in increased. 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. <Problem to be solved by the invention> However, in conventional fault diagnosis systems using expert systems, (a) the knowledge is not versatile and fault diagnosis cannot be performed for various targets, 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 actinitor, 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, it is necessary to predict possible failures at the design stage and include a mechanism for dealing with such failures; unknown failures cannot be handled. (4) For the reason of (3) above, manipulate the parameters of any actuator.

[Unable to predict possible side effects on other actuator parameters. 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.
Fault diagnosis was performed based on the set C of sensor C and actuator C, and thus fault diagnosis was performed based on each independent sensor and actuator set, and fault repair was performed based on the diagnosis. 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 has occurred, a failure diagnosis method for inferring the failure symptoms, cause of failure, and equipment status, failure symptoms that may occur in the target equipment, cause of failure, and the current state of the failure. Cases that include the status of the device, the work required for repair, the number of successful applications, and the number of failed applications, or at least based on case storage means stored by cause of failure, failure symptoms and causes of failure diagnosed by failure diagnosis means. A case detection means for detecting an applicable case from among the cases stored in the storage means, a case detection means for detecting an applicable case from among the cases stored in the storage means; By comparing the current device status and the device status in each case, or by comparing the number of successful applications and the number of failed applications in addition to the comparison, which one of the detected multiple cases is applied preferentially. a priority determination means for determining whether or not to perform a repair work; a repair work execution means for performing a work included in a case detected by the case detection means and given a priority by the priority determination means as a failure repair work; and a repair work. After completion, a repair result determination means detects the state of the target device and determines whether the failure has been removed, and in response to the repair result determination means determining that the repair has been successful, detects the current failure of the device. The present invention is characterized in that it includes a case addition means for additionally storing a case including a symptom, a cause of failure, a state of the device, and an executed work as a new case in the case storage means. , cases are organized and stored at least by cause of failure.For example, cases where the cause of failure is ``incorrect halogen lamp settings,'' cases where the cause of failure is ``incorrect main charging voltage,'' and cases where the cause of failure is ``incorrect developing bias.'' The cases are organized and memorized, such as... 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, first, 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, a display using a plurality of parameters, priority is given to cases in which the number of parameters that match the state is large. In addition, if the inferred current device state and the device state in the case are equal, the case with the highest number of successful applications is given priority, and if the number of successful applications is equal, the case with the lowest number of failed applications is given priority. The application order is determined according to the following. Then, the work included in the case detected by the case detection means and prioritized by the priority order determination means is executed as the failure repair work. After the repair work is completed, it is determined whether the fault has been removed or not. If the fault has been removed, the repair is determined to have been successful. The cases including the above are additionally stored as new cases, and the number of cases is increased. <Example> System configuration v! 1. 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, lb, 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. Information taken in from each IC 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. The system control circuit 10 includes a digital signal/symbol conversion section 11, a fault diagnosis section 12, a fault simulation section 13, a target model storage section 14, a repair 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,
Converting the signal provided from the lc into such qualitative information symbolized facilitates the approach to fault diagnosis. Note that the symbols are not limited to normal, high, and low as in this example, but may be other expressions such as on and off or A, B, C, and D. When the digital signal is converted into a symbol in the conversion unit 11, characteristic data specific to the target machine stored in the target model storage unit 14 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,
How the repair planning section 15, case base storage section 17, and work script storage section 18 perform fault diagnosis, fault simulation, 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.
6c 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 in FIG. 1 will be explained. The detection signal of the sensor la, lb or IC is amplified,
and is converted into a digital signal and read into the system control circuit 10, for example, every predetermined read cycle (
Step S1. ). 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, the output ranges of the sensors la, lb, and lc are set in the target model storage unit 14 as reference value data specific to the target machine as follows. That is, sensor 1a: output less than ka1 - low to ka2 - exceeds normal - less than high - low - kb2 - exceeds normal - high Sensor 1b: output ka output ka2 output kb output kbl output kb2 sensor 1C/output less than kc1 - low output kc1 to kc2 - exceeding normal output kc2 - set to high. The digital/symbol conversion unit 11 converts the sensors 1a to 1c based on reference value data specific to the target machine set in the target model storage unit 14.
Converts the digital signal from the device into a symbol of ``low,''``normal,'' or ``high.'' Next, in the fault diagnosis section 12, the converted symbol is evaluated, and the presence or absence of a fault is determined and the fault symptoms are specified (step S3). The target model storage unit 1 is used to determine the presence or absence of a failure by evaluating symbols and to identify failure symptoms.
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 qualitative data that qualitatively represents the behavior or attributes of each element that makes up the device and the connection relationships between each element,
In the fault diagnosis section 12, a parameter causing the fault is searched, and in the fault simulation section 13, assuming that the searched parameter is the cause of the fault,
Fault conditions are simulated. Furthermore, the failure diagnosis section 12 compares the simulation results with the current parameter values to determine the validity 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 determining the presence or absence of a failure, diagnosing the failure, and simulating the failure state, the failure symptoms and 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, "copied images are pale", etc.), and the failure cause is the object that causes the symbol to change. This is a change in the mechanism or structure of a machine (for example, in the case of a copying machine, a decrease in the amount of light from 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). Then, a case similar to the current state of the target machine is detected (step S6). Detection of this case is performed based on whether the failure symptom and the failure cause match. Then, based on the detected case (repair work is executed (step S7). In the repair work, the case is corrected and the repair work is modified as necessary, and the corrected case is used 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), the process ends. A method inference is made (step S9), and side effects are further simulated (step 5).
IO), a repair plan is determined, and repair work based on the decision is executed (step 511). Although the inference and work execution in steps 89 to Sll are not based on cases, if the repair work based on this inference is successful, the repair results are 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. Configuration and state of target F11 tower 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 for measuring the density of the image copied on paper. This is a concentration meter for Although not shown in FIG. 3, three types of actuators are provided. That is, the main charging volume VR1 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 ceremony shown in Table 1 will be referred to as the "substantive model." Furthermore, the representation shown in FIG. 4 in which the entity model is abstracted and expressed as a connection tree of each parameter will be referred to as a "mathematical model." 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. The target model storage unit 14 also stores information regarding predetermined parameters among the parameters included in the real model.
For example, reference value data measured at the time of factory shipment is stored. This reference value data is characteristic data specific to this image forming apparatus. For example, in this machine, as shown in FIG. 5, for the parameters x, v, , o, , v,
Reference value data specifying the normal and high ranges is stored. 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. The target model storage unit 14 also stores evaluation function knowledge as an example of fault diagnosis knowledge, which is a standard for determining whether or not the target machine is operating normally, based on the converted symbol. has been done. Note that the evaluation function processing, in other words, the failure diagnosis knowledge may be unique to the target device, or may be not unique but common to a wide range of image forming apparatuses. Evaluation function knowledge includes the following knowledge. Image density O, -normal, degree of fogging 0.・Normal, separation performance Sll normal here, 0. ．． 0.・, S, or if the above conditions are not met, it means that 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 X-30 Surface potential sensor value V, -300 Densitometer value O, -7 Also, densitometer value O, - fog degree O1 when using a blank document with optical density D-0, Surface potential sensor value V when the halogen lamp is turned off
, -dark potential V, ,, and their values are respectively fogging degree O, -50 dark potential ■. Suppose it was -700. The fogging degree O1 and the dark potential V may be measured manually, or may be measured by a sensor under certain conditions, for example, each time the target machine is turned on or before copying starts. It may be programmed to be measured automatically. In this embodiment, the latter is adopted. AE sensor 1 a s Each point X, V, , O,, O5, obtained by surface potential sensor 1 b and densitometer 1 c
Vo is the digital signal/symbol converter 1, respectively.
1, it is converted into a symbol. 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 V, M-Low Q, m-Low Vo-Normal In the fault diagnosis unit 12, each of these symbolized parameters is used for functional evaluation 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 O7 is not normal, it is determined that there is a failure, and the failure symptom is ``Image density is too low (0
．． − low)”. And next,
0. - Low" as a failure symptom, a failure diagnosis, that is, the 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, and parameters that may cause 0, -low are searched for. In the mathematical model in Figure 4, 0. Fig. 6 shows the parameters that have the potential to lower J. In Fig. 6, the parameters marked with upward or downward arrows are the parameters that may cause the parameter O,-low, and those with upward arrows are those with downward arrows when the parameter increases. becomes 0. if that parameter decreases. -Causes low. Next, each parameter,ζ,D,that can cause a 0,-rho was explored in the mathematical model. ■,97o,Vb,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, ■, -Low, -→Defective transfer transformer ζ -Deterioration of low knee paper ■, -Defective high knee developing bias γ0 -Deterioration of low knee toner -Vo -Defective low knee main charging voltage HLIl-High: → of halogen lamp Defective setting D--Low: → When the original is thin VL-Low, the transfer transformer is defective, ζ-Low indicates deterioration of the paper, ■, -High means the developing bias is defective, etc. This knowledge is failure cause knowledge, and this knowledge is included in qualitative data common to image forming apparatuses. 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, that is, the cause of failure is, for example, a defective transfer transformer, and vl-low is set for a model in a normal state. Then, the influence on each parameter in that state is examined using a mathematical model. - If set to low, 0. -low and S2-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-口- is set on the mathematical model of the normal state and the result is compared with the symbol 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 failure cause candidates, and it is confirmed whether or not the inference of the failure cause is correct. 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 concluded 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: Halogen setting failure If the states of the parameters shown in Table 2 are traced onto a mathematical model, 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 S5 in the flowchart in FIG.
．． S6. This corresponds to S7 and S8, and specifically represents the contents of the repair process in detail. Search for Cases According to the above-mentioned fault diagnosis method, the cause of the fault that causes the occurring fault symptom is inferred (step 521).
. Based on the unity, a large number of cases stored in the case base storage unit 17 (see Fig. 1, 2) 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 cases stored in the case base storage unit 7 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 15 uses the failure symptoms diagnosed by the failure diagnosis unit 12 and the failure simulation unit 13 to
Picture @! By using "concentration too low (05-low)" and failure cause "improper halogen lamp setting (HL-high)" as indexes, a case that satisfies both the failure symptom and the failure cause is detected. Therefore, even if the failure symptom is "low image density," cases where the cause of the failure is, for example, "defective main charging voltage" will not be 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 are not considered "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 "
Suppose that as a result of searching for a case based on ``Poor halogen lamp setting'', case (1) shown in Tables 4 to 6 of section f is detected. (Left 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 parameter status before each restoration in cases (1) to (3) with the current parameter status (Table 2), only the v6 status is different in case (1). In case (2), the states of va and V are different. In case (3), the states of Vfi, ■, 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 account (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 the same, and the number of successful applications is also the same, the number of failed applications is taken into consideration (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 cause of the failure "-\Incorrect Rogen lamp setting" 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 the nost has been increased. 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 situation is the parameter HL -t\i, and in this state, by performing the antecedent operation of decreasing the lamp volume AVR. , parameter HL-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, then the work with the number written in the repair work column of case (1) is "Improper Lamp Setting" work script is selected and executed (step 528) (actually, 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 the work being executed, the work is determined to be successful (YES in step S29);
Furthermore, it is 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 S3
0 and No), the value in the column of 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 333), if any (Y in step 333);
ES), step S26 for the next priority case.
Processing is performed from If there is no case with the next priority (No in step 533), a repair plan is inferred taking into account the side effects that will be detailed later, that is, OMS processing is performed (step 534).
). Then, it is determined whether or not the 0MS processing was successful (step 535), and if it is determined that the 0MS processing was successful (¥ES in step S35), a new case is determined based on the data obtained by the QMS processing. Created, Part 1
One example is registered in the case base storage unit 17' (step 536). Then, the process ends. 0MS processing ends in failure), (step S3
5), no new case is registered and the process ends. Now, as mentioned above, the parameter situation before repair in case (1) does not completely match the current parameter situation, and the states of parameters v, , are inconsistent, so the actual processing is performed in step It was judged as 82'7 No.
The process advances to step S37 shown in FIG. 8B. 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 H5-high, so the antecedent status matches the current parameter status. If they match (YES in step 538)
, its work 1 is executed (step 539). Then, it is determined whether the task is successful or not (step 540), and if the consequent status is obtained as a result of the task execution, it is determined that the task 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 entered in the repair work column of case (1) (step 541), and if there is a next work (
YES in step 541), the next work is specified, the antecedent status of the work and the current parameter status are compared (step 537), and as described above, step S38
The subsequent processing is repeated. If there is no more work to do (No in step S41)
), the state of flag At or B (the purpose of these flags A and B will be described later) is determined (step 542), and if neither flag A nor B is 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 543), 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 value, the consequent part status still remains. If the parameter HL-normal cannot be obtained, it is determined that the task 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 the 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 cause of the failure is "Incorrect halogen lamp settings (H11-1)". Among these cases, the repair work column shows the work number that was determined to be a failure, such as the work All instances in which the number l 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 difference between the parameter status before repair 1 and the current parameter status is 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), but the condition of the parameters before the repair and the current state of cases (1) and (2) are The parameter situations are compared with each other, and parameter V7 is taken up as a common difference. Zunai) Chi,
In the parameter situation before repair, the parameter V,, -low is common, but in the current parameter situation,
Parameter V6 - Normal. If it is determined that there is a different parameter (YES in step S51), that parameter, that is, the parameter V, - normal in the specific example, is hypothesized to be the cause of the failure of the current operation, and this parameter V, is set to normal. A job that can be set to low from
step 853). Looking at the work script in Table 7, work 5 results in parameter ■. Since it can be seen that it is possible to change from normal to low, it is determined that there is work to be done (Y in step 553).
ES). In this case, the work column of case (1) is temporarily corrected and work 5 is inserted. Further, in order to indicate that this tentative revision W has been made, a flag B is set (step S
54). Operation 5 is then executed (step 55
5). If the result of execution of work 5 is V, -guchi, then 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 work script shown in Table 7 is corrected so that vn-row is added to the antecedent status of work 1, 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 VY1 low.]. (The following is a blank space) 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 done (NO in step S40).
), 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.
is created and registered. Additionally, flags A and B are reset (step 544). This new case (1
-1) are shown in Table 9. (Leaving space below) Table 9 Case (1-1) shown in Table 9 differs from case (1) shown in Table 4 in the parameter ■ in the parameter situation before repair. - It is normal, and in the repair work column, r5. The point is that two tasks 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 358).
, if there is work, the processing from step S54 is performed. Furthermore, if it is determined in step S51 that there are no different parameters, or if it is determined in step S53 that there is no work, flags A and B are reset (step 559), and then other cases, i.e. It is determined whether there is a next application priority case (step 560). 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 560 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, if the repair plan based on case (1) fails, case 1 is applied, and case (2), which is a case with the next application priority.
) Consider the case where a restoration plan is carried out based on In this case, in step S61, case (2) and case (
Corresponding to 2), the selected failure cause "/10 gen lamp setting failure" work script is set in the work register. Next, work 1 of the work script is specified by the "]" displayed in the repair work column of case (2), and its antecedent status and current parameter status are compared (step 5).
37), a match or mismatch between the two is determined (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 H becomes normal, it is determined that the work has been successful (YES in step S40), and it is determined whether there is a next work (step 541). In case (2), since there is work 2 as the next work, the process proceeds to step S37, where 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 status v1-guchi of work 2 does not match the current parameter status V-normal (NO at 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 by operation 4, the parameter V can be made low when H is 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 by the temporary correction is executed (step 548), and it is determined whether or not work 4 was successful (step 540). If the execution of this work 4 is successful (step S40
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, in step 538, the determination is YES, 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 S41,
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 step S
The case in which the repair work column was temporarily corrected in step 47 is registered as a new case (2-1) (step 544). Also, 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 in the situation before repair, the parameters ■. - It is normal, and the repair work is N, 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 status with the antecedent status (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 objects 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 such parameters while running, performs a type of learning using this, and tunes the knowledge 1. Even if a change occurs between the above parameters, repair work can be performed to effectively deal with the change. In other words, if the relationship between the parameters of the target machine changes, the example is modified based on the change, a new example is created, and the contents of the work script are 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 the repair plan Next, inference of the repair plan, step 55 in FIG.
The OMS processing shown in 1 will be explained. As a result of fault determination, "image rg! density is too low (0°-low)" was identified as the fault symptom, so the goal of repair was 0. is to increase by 1. Therefore, from the relationship in the mathematical model shown in Figure 4, it is inferred that Oo, which is the repair target, can be increased by increasing D, Vl, or ζ. Ru. Next, when inference is made with the goal of increasing D6, ■
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. γo: The sensitivity of toner cannot be changed, and ζ: The sensitivity of paper cannot be changed either. Furthermore, in this specific example, (b): The bias voltage cannot be changed because there is no actuator. Of course, by adding an actuator, ■,
can be made changeable. Furthermore, X: Logarithm of the amount of light reflected from the original, ■: The surface potential of the drum after exposure and the toner density on the double drum cannot be changed per se, but can be changed indirectly by changing other parameters. It can only be changed, and is excluded from repair candidates here. Although not directly related to this specific example, A1. :The amplitude of the separation AC voltage can also be changed by adding an actuator. As described above, in this specific example, vl : transfer voltage Vo : surface potential after main charging H5 : logarithm of halogen lamp output light amount is taken up as a repair candidate. On the other hand, the target model storage unit 14 stores the following knowledge in advance as repair plan knowledge. In other words, (a) Increasing V → Transfer transformer controller (b) (C) (d) (e) Increasing the troll voltage ■ Decrease the control voltage of the transfer transformer Decrease the control voltage of the transfer transformer Increasing V7 Increase the control voltage of the charging transformer Decrease V7 → Decrease the control voltage of the main charging transformer Increase HL Shift the halogen lamp control signal to the high voltage side (f) Decrease HL - lower the halogen lamp control signal Shift to 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 planning knowledge to the correction mN complement obtained based on the mathematical model, 0. Modification muff to increase
(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 → Overwhelm the halogen lamp control signal with low voltage. There are three ways to shift to. If you simply want to increase the image density O0, these three
Repair is possible by performing any of the methods. However, it is conceivable that the target machine will be affected by various side effects by increasing the image density O. 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 05 on the mathematical model when D-〇 is used), (c) V, 11 and 12 (Figure 12 shows 05 on the mathematical model when D-0), (
f) When HL is lowered, see Figures 13 and 14 (
In FIG. 14, O5 when D-0 is expressed 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, there is a case where Ol.>Normal. In other words, fogging may occur. (c) S becomes > normal, and there is a possibility that a separation failure will occur. (2) When V is increased (FIGS. 11 and 12) (a) Output image density increases. (b) When D-0, Ol.>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 found in 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 estimating the cause of a failure, estimating a repair plan is based on the premise that the device is not in a faulty state but is normal, traces the state of the device on a mathematical model, and performs 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 HL cannot be selected, for example, if the AVH volume for shifting the halogen lamp control signal to the lower voltage side is already at the lower limit, then The repair plan (2) of increasing V7, which has the least impact, is selected. However, if the repair plan of increasing . Which parameter should be manipulated in order to lower . . As a result, it is selected whether to raise HL, lower (1), or lower (2), 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 ) Unrolling is based on the knowledge that anything that forms a loop will stop unrolling at that point. In FIG. 15, in the end, two repair plans remain: (1) a loop of V, ↑→HL ↑→Vo↑, and (2) a loop of V, , ↑→Vl ↓→Vll↑. Now, suppose that when the loop (1) is performed as a restoration plan, the image density becomes a proper density, that is, 0, which becomes normal. In such a case, since ■ and HL have been increased, the value of the surface potential measured by the sensor 1b in the pre-repair state where the image density O2 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 as a result, a rash occurs on the resulting copy, the lamp volume AVR is operated to increase the halogen 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 lamp volume AVR alternately and appropriately, when the image density became normal, that is, when parameter 01 became normal, it was obtained from the detection output of the densitometer, which is the sensor IC. When the repair process is finished. Furthermore, if the above two repair plans are infeasible, the repair plan (3) of increasing vl is selected, and the two side effects of this are fogging and poor separation. A fault diagnosis is performed based on the assumption that 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 01 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, take only one repair plan out of the three, simulate the side effects that may occur if the actuator means is actuated based on that repair plan, and It is determined whether the negative influence can be removed by actuating an 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, if for the first selected repair plan, a side effect is simulated and it is determined that the simulated side effect cannot be removed by actuating another actuator means. If it is done,
If the first repair plan is abandoned and a second repair plan is taken up, the selected actuator means may be activated based on the second repair plan. determine whether the simulated side effect can be removed by activating an actuator means other than the actuator means, and if the side effect can be removed, the Perform restoration work based on the second restoration plan. 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 large a side effect, 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 considerably limited due to the small number of actuator parameters, but by increasing the number of actuator parameters, the flexibility and possibilities 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 (reference values shown in FIG. 5) of each parameter be updated and the parameters be symbolized based on the new reference value data. In addition, in the above-mentioned specific example, although the operating range of each actuator was not specifically mentioned, the target model storage unit 1
If the operating range data that sets the operating range of the actuator is included in the device-specific characteristic data stored in step 4, the actuator will be operated when the output state of the actuator is within the stored operating range. If it can be determined that it is possible and 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 above specific example, the system is completely autonomous, that is, the system automatically performs self-diagnosis to determine whether there is a failure or not, and self-repairs if a failure occurs, without any operation by service personnel or users. I picked it up and explained it. However, in the present invention, the sensor is removed from the configuration requirements, and a configuration is adopted in which a service person or the like can measure functional status data at predetermined locations of the image forming apparatus and input the measured data into the apparatus. By doing so, 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, 0) an image forming apparatus that includes a fully autonomous self-diagnosis and self-healing system; (2) an image forming apparatus that has an autonomous self-diagnosis system and a non-autonomous self-healing 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,
Only actuators that are actually adjustable may be selected by considering the adjustment range of the actuator. To explain more specifically, the actuator is, for example, A
For VH, the lower limit value of AVR is "0" and the upper limit value is rlo
oJ, and the configuration is such that the AVR setting state can be detected as an integer value between O and 100. In addition, the upper limit value “0” of AVR and the upper limit value r1 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 AVR is understood as integer value data from 0 to 100 corresponding to the adjusted state. The repair planning unit 15 uses integer value data from O to 100 obtained depending on the adjustment state of the AVH
It is determined whether the AVR can be selected as an 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. According to this invention, it is determined whether or not a failure has occurred in the image forming apparatus, and when a failure has occurred, the failure symptoms,
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, the most suitable case for fault repair is detected, and fault repair processing is performed based on the case. In case-based failure repair processing, the repair plan is registered in the case in advance, so
It is not necessary to infer a repair plan, the time required to start repair processing can be shortened, and the image forming apparatus can perform fault diagnosis and fault repair in a short period of time as a whole. Further, according to the present invention, since the cause of failure is determined based on qualitative data common to image forming apparatuses, a self-diagnosis and self-repair system that can handle unknown failures that are not explicitly described is provided. The image forming apparatus can produce a white image. Further, the self-diagnosis and self-repair system according to the present invention is applicable to a specific image forming apparatus (rather than 7).
The present invention 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. <Effect of the invention>

[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 fault diagnosis 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, la, lb, 1cm...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. Cases including the work required for the process, the number of successful applications, and the number of failed applications 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 detected by the case detection means, 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 each case A priority method for determining which case to apply preferentially among a plurality of detected cases by comparing the state of the device at , or by comparing the number of successful applications and the number of failed applications in addition to the comparison. a priority determination means; a repair work execution means for executing a work included in a case detected by the case detection means and given a priority by the priority order determination means as a failure repair work; A repair result determining means detects the condition and determines whether the fault has been removed, and in response to the repair result determining means determining that the repair is successful, the present device failure symptom, failure cause, and device status are detected. A self-diagnosis and self-repair system for an image forming apparatus, comprising: and case addition means for additionally storing a case including the executed work in a case storage means as a new case.