JPH04130347A - Self-diagnostic system for image forming device - Google Patents

Abstract

PURPOSE:To enable invariably excellent automatic self-diagnosis by deciding whether the device is normal or abnormal according to stored feature data and fault diagnostic knowledge corresponding to state data from a sensor means and updating the feature data according to the state data from the sensor means when the device is normal. CONSTITUTION:A fault diagnostic part 12 decides whether the device is normal or abnormal according to the state data on the device which are inputted from sensors 1a - 1c and the feature data and fault diagnostic knowledge in an object model storage part 14. Then when the device is normal, the reference data of the feature data in the storage part 14 is updated according to the state data from the sensors 1a - 1c to perform the excellent automatic self-diagnosis based upon the latest data at all times.

Description

DETAILED DESCRIPTION OF THE INVENTION <Industrial Application Field> The present invention relates to a self-diagnosis system for an image forming apparatus. More specifically, the present invention relates to a device or system that self-diagnoses the operational status of an image forming apparatus 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, 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 (3) above, it is not possible to predict the side effects that may occur due to the manipulation of the parameters of any actuator on the parameters of other actuators.

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 the sensor C and actuator C, respectively, and the 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 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 system for an image forming apparatus that embodies image data to generate a visible image, and the system includes feature data representing the characteristics of the image forming apparatus, A forming device is expressed as a combination of a plurality of elements, and qualitative data qualitatively representing the behavior or attributes of each element and the connection relationship between each element, a storage means in which fault diagnosis knowledge is stored, and a memory device stored in advance of the image forming device. The image forming apparatus uses a plurality of sensor means for detecting the functional state at a predetermined location and outputting the state data, and the image forming apparatus based on the state data given from the sensor means and the characteristic data and fault diagnosis knowledge stored in the storage means. A failure determination means for determining whether the image forming apparatus is normal or abnormal; in response to the failure determination means determining that the image forming apparatus is abnormal, the failure determination means determines the failure based on the qualitative data and failure diagnosis data stored in the storage means; In response to the failure diagnosis means for inferring the cause and the failure determination means determining that the image forming apparatus is normal, the characteristic data stored in the storage means is determined based on the status data provided from the sensor means. The apparatus is characterized in that it includes a data update means for updating predetermined data.

According to the present invention, it is determined that a failure has occurred when the status data provided from the sensor means becomes relatively low or relatively high compared to, for example, reference data as characteristic data.

When a failure occurs, the cause of the failure is inferred based on the qualitative data stored in the storage means and the failure diagnosis knowledge, and which of the components of the image forming apparatus is abnormal. If there is, it will be output.

Further, when the image forming apparatus is determined to be normal by the failure determination means, predetermined data, such as reference value data, of the characteristic data stored in the storage means is updated based on the given status data. , characteristic data that always represents the latest state of the device is stored in the storage means.

Qualitative data is not data specific to the image forming apparatus, but data that is common to many image forming apparatuses and is qualitatively expressed. Therefore, the self-diagnosis system according to the present invention It can be easily incorporated into an image forming apparatus.

<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 a plurality of sensors la, ], b installed on the target machine.

A plurality of actuators 6a, 6b, and 6c are included for changing the functional states of the IC and 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.

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 conversion function is provided for converting a digital signal into one of three symbols, normal high and low, for example. By converting the signals provided by the sensors la, lb, and lc into such qualitative information that is symbolized, the approach to fault diagnosis becomes easier. Note that the symbols are not limited to normal, high and low as in this example, but also on and off or A, B, C.
Other expressions such as and D may also be used. 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 a fault diagnosis is performed. This is a component that expresses and outputs the failure state of a machine using qualitative information, that is, symbols.

The repair planning unit 15 is a component for inferring a repair plan for repairing a failure when a failure occurs and deriving a repair work. In inferring the repair plan and deriving the repair work, qualitative data (described in detail later) stored in the target model storage unit 14 is utilized.

The methods of fault diagnosis and fault simulation, inference of a repair plan, and derivation of a repair work in the fault diagnosis section 12, fault simulation section 13, and repair planning section 15 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.

Then, the digital signal is converted from a digital signal to an analog signal by the D/A conversion circuit 4 and is provided to the actuator control circuit 5. Actuator control circuit 5
is a plurality of actuators 6a, 6b based on a given analog signal, that is, an actuator control command.
, 6c are selectively activated to perform the 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.

The detection signal of sensor la, lb or 1c is amplified,
and is 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, 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: less than output ka1 - output kal~ka2 exceeds normal output ka2 - high sensor 1b = less than output kb+ - low output kb, ~kb2 - exceeds normal output kb2 - high sensor 1c: output less than kc1 - low output kcl~kc2 - exceeds normal output kc2 - is 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 ``low J r normal'' or ``high'' symbol.

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 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, the failure diagnosis unit 12 uses the qualitative data stored in the target model storage unit 14 that qualitatively represents the behavior or attributes of each element constituting the device and the coupling relationship between each element. , the parameter causing the failure is searched, and the failure state is simulated in the failure simulation section 13 on the assumption that the searched parameter is the cause of the failure.

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 the failure determination, failure diagnosis, and failure state simulation, the failure symptoms and failure causes of the target machine are determined (step S5). Here, failure symptoms include the output status of the target machine (for example, in the case of a copying machine,
The cause of the failure is,
This is a change in the mechanism or structure of the target machine that causes the symbol to change (for example, in the case of a copying machine, a ``decrease in the light intensity of a halogen lamp'', etc.).

Next, the repair planning unit 15 infers a repair method. In this inference, a simulation of the influence of repair is also performed (step S6).

Then, a repair plan is determined (step S7), and control instructions are developed based on the determined repair plan (step S8). When developing actual control commands, characteristic data unique to the device, such as limit values for actuator operation, is read out from the target model storage unit 14 and utilized.

The developed control command is converted into an analog signal and given to the actuator control circuit 5, and repair control is executed (step S9). Control then ends.

Next, we will explain how to infer failure diagnosis and repair plans.
A detailed explanation will be given with reference to specific examples.

In the following description, 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 used as the target machine.

Explanation using a specific target machine as an example 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 le 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 IC measures 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 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."

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 "Substance 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, as shown in FIG. 5, this machine stores reference value data specifying low, normal, and high ranges for parameters x, v, , o, , v, 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, which is a standard for determining whether or not the target machine is operating normally, based on the converted symbol. There is.

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 X-30 Surface potential sensor value V, -300 Densitometer value O, -7 Also, densitometer value O1 when using a blank document with optical density D-0 - fog degree O1. Surface potential sensor value V when the halogen lamp is turned off
, -dark potential v0, and their values are, respectively, fogging degree O1·-50 dark potential V, -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 6 values obtained by surface potential sensor 1 b and concentration meter IC X5v1.08, O8・,v
o are the digital signal/symbol converters 11, respectively;
is converted to a symbol at .

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 Vn1m In the normal fault diagnosis unit 12, each of these symbolized parameters is combined with functional evaluation knowledge as an example of fault diagnosis knowledge stored in the target model storage unit 14. be compared. As a result, since the image density O6 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, r
A failure diagnosis, that is, an inference of the cause of the failure, is performed using ``o, -口-'' as a failure symptom.

Fault diagnosis is first performed in the fault simulation section 13 using the mathematical model shown in FIG. 4, and parameters that may cause ○ and -low are searched for.

In the mathematical model in Figure 4, 0. Fig. 6 shows the parameters that have the potential to reduce the . 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 decreases, 0. -Causes low.

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

Vl, 7o, 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, V, - Low knee transfer transformer defect ζ - Low knee paper deterioration ■, - High knee developing bias defect γ. -Low: → Toner deterioration■6-Ro2-Main charging voltage failure HL-High: →Halogen lamp setting failure D -Low:
→The original is thin. 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. -mouth- and S, -mouth-, 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 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.

Next, we will explain the reasoning behind the restoration plan.

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
0. The goal is to increase the

Therefore, from the relationship on the mathematical model shown in Figure 4, it is possible to increase the repair target 0 by increasing D, increasing ■, or increasing ζ. It is inferred.

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 3.

(Margin below) Table 3 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.

γ. : Toner sensitivity cannot be changed, ζ: paper sensitivity is also unchangeable.

Also, in this specific example, ■,: Bias voltage It is also impossible to change 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, the amplitude of the AC voltage for separation into two can also be changed by adding an actuator.

According to the above, in this specific example, as repair candidates, (1): transfer voltage (2): surface potential HL after main charging; logarithm of halogen lamp output light amount is taken up.

On the other hand, the target model storage unit 14 stores the following knowledge in advance as repair plan knowledge. In other words, (a) Increase the control voltage of the transfer transformer to increase V. (b) Decrease V → Decrease the control voltage of the transfer transformer. (c) Increase the control voltage of the priest charging transformer to increase V. (d) Decrease V, lower the control voltage of the main charging transformer (e) Increase HL → shift the halogen lamp control signal to the high voltage side (f) Decrease HL → lower the halogen lamp control signal Shift to the 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 knowledge to the repair candidates obtained based on the mathematical model, 0. As a repair operation 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 → Halogen Three methods are available for shifting the lamp control signal to lower voltages.

Image density 0. If you just want to increase
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 O1. 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. 8 to 13 are obtained. In other words, (a) when V is increased, Figures 8 and 9 (Figure 9 shows O5 when D-0 is expressed on a mathematical model), (c) when V is increased. When increasing
f) When HL is lowered, see Figures 12 and 13 (
In FIG. 13, 05. when 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 8 and 9) (a)
Output image density increases.

(b) When D-0, there is a case where Ol.>Normal.

In other words, fogging may occur.

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

(2) When V is increased (Figures 10 and 11) (a) Output image density increases.

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

(3) When HL is lowered (FIGS. 12 and 13) (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 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 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 V, which has the least impact, is selected.

However, if a repair plan that increases vo is selected, a side effect of the possibility of fogging is predicted, so in the mathematical model shown in Figure 11, in order to lower O, Which parameters should be manipulated is examined based on the mathematical model shown in FIG. 11, 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 (2), and a repair plan including prevention of fogging is carried out.

That is, as shown in FIG. 14, the reasoning for the repair operation is developed assuming side effects. In the inference development of repair operations as shown in Figure 14, (a) branches that contradict the previous repair plan on the mathematical model are not selected (b) branches 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 Figure 14, after all, (1) V, ↑→HL ↑→■ゎ↑ loop, and (2) V, ↑−V8 ↓−■. Two repair plans remain, the loop above.

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,■. and H5 have been increased, so in the post-repair state where the image density returns to normal, the value of the surface potential measured by sensor 1b changes to a much higher value than the value initially measured. They should have done so. 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 became normal, that is, when the parameter O8 became normal, it was obtained from the detection output of the densitometer that is the sensor 1c. At this point, the repair process is terminated.

Furthermore, if the above two repair plans are infeasible, the above-mentioned repair plan (3) of increasing vI 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, 0. The end of the repair is determined when it 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 actuated 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, when a certain 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, or the flexibility and possibilities of repair can be further improved by increasing the number of actuator parameters.

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, but it is 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 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 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, (1) an image forming apparatus having a fully autonomous self-diagnosis and self-healing system; (2) an image forming apparatus having 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.

<Effects of the Invention> According to the present invention, when a failure occurs in the image forming apparatus, it is possible to provide an easily repairable image forming apparatus that can accurately infer and output the cause of the failure. .

Therefore, a service person or the like can quickly perform repairs based on the output cause of the failure. In other words, it is sufficient to operate the actuator of the image forming apparatus according to instructions or replace broken parts.

Further, according to the present invention, since the cause of failure is determined based on qualitative data common to image forming apparatuses, the image forming apparatus has a failure diagnosis system that can handle even unknown failures that are not explicitly described. Equipment can be taken.

Furthermore, the failure diagnosis 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, resulting in an inexpensive failure diagnosis system. An image forming apparatus having the following 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 fault diagnosis on a mathematical model. FIGS. 8 to 13 are diagrams showing the development of the secondary influence inference on the mathematical model. FIG. 14 is a diagram showing operations when selecting a repair plan. FIG. 15 is a diagram showing updated reference value data. In the figure, 1a, 1b, 1c--sensors, 5a. 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,
shows.

Claims (1)

[Scope of Claims] 1. A self-diagnosis system for an image forming apparatus that embodies image data to generate a visible image, comprising: characteristic data representing characteristics of the image forming apparatus; and a plurality of image forming apparatuses. A storage means in which qualitative data qualitatively representing the behavior or attributes of each element and the connection relationship between each element and failure diagnosis knowledge are stored, and a functional state at a predetermined location of the image forming apparatus. a plurality of sensor means for detecting and outputting state data, and determining whether the image forming apparatus is normal or abnormal based on the state data provided from the sensor means, characteristic data stored in the storage means, and fault diagnosis knowledge. A failure determination means for determining a failure, and a failure for inferring the cause of the failure based on qualitative data and failure diagnosis data stored in a storage means in response to the failure determination means determining that the image forming apparatus is abnormal. In response to the diagnosis means and the failure determination means determining that the image forming apparatus is normal, predetermined data from among the characteristic data stored in the storage means is determined based on the status data provided from the sensor means. A self-diagnosis system for an image forming apparatus, comprising: a data update means for updating.