JP2579048B2 - Self-diagnosis and repair system for image forming equipment - Google Patents

Abstract

PURPOSE:To enable easy fault repair by deciding abnormality according to stored fault diagnostic knowledge in response to the input of state data showing the function state of the device, reasoning the cause of the fault, and removing the cause of the fault by an actuator. CONSTITUTION:In response to the data from sensors 1a - 1c which shows the function state of the device, a fault diagnostic part 12 decides whether the device is abnormal or not according to the fault diagnostic knowledge in an object model storage part 14 and also reasons the cause of the fault according to fault diagnostic data in the storage part 14. A repair planning part 15 varies the function state of the device according to the output of the diagnostic part and selects plural actuators 6a - 6c which can remove the cause of the fault, thereby easily repairing the fault.

Description

Description: TECHNICAL FIELD The present invention relates to a self-diagnosis and repair system for an image forming apparatus. More specifically, the present invention relates to an apparatus and a system that can self-diagnose and repair the operation state and the like of an image forming apparatus using artificial intelligence and knowledge engineering, which have been actively studied in recent years.

<Conventional technology> In the field of development of precision machines and industrial machines, the latest artificial intelligence (artificial intelligence: so-called AI) is used to save labor for maintenance work and prolong the length of automatic operation.
Research on expert systems using technology has been actively conducted. Some expert systems perform self-diagnosis as to whether or not a failure has occurred in the device and self-repair the resulting failure.

<Problems to be Solved by the Invention> However, in the failure diagnosis system using the conventional expert system, (a) the knowledge is not versatile, failure diagnosis cannot be performed for various objects, and (b) unknown failures. (C) If the target becomes complicated, the amount of knowledge required for failure diagnosis will explode, which makes it difficult to realize, and (d) difficulties in acquiring knowledge. Was pointed out.

More specifically, a conventional automatic adjustment system or fault diagnosis system basically operates a corresponding actuator based on an output of a certain sensor. That is, a kind of automatic adjustment and failure diagnosis have been performed by a combination of a predetermined sensor and actuator. Therefore, basically, a certain sensor corresponds to a specific actuator, and the relationship between the two is fixed. Therefore, (1) The relationship between the parameters of the sensor and the parameters of the actuator must be specified numerically.

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

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

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

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

And so on.

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

The present invention has been made in the background of the related art, and an object of the present invention is to provide a new self-diagnosis and repair system for an image forming apparatus that solves the disadvantages of the related art.

<Means for Solving the Problems> The present invention is a self-diagnosis and restoration system for an image forming apparatus that embodies image data and generates a viewable image. The qualitative data represented qualitatively using, reference value data specifying a qualitative value category for a predetermined parameter among the parameters, and, for a specific parameter among the parameters, a condition that the parameter can take. Storage means for storing fault diagnosis knowledge to be set; data input means for inputting a quantitative value indicating a functional state at a predetermined location of the image forming apparatus; and storing the quantitative value input from the data input means. Means for converting to qualitative state data by comparing with the reference value data stored in the Failure determination means for comparing the data with the failure diagnosis knowledge stored in the storage means to determine whether the image forming apparatus is normal or has a failure symptom; the failure determination means has a failure symptom in the image forming apparatus; In response to the determination, the fault data is applied to the qualitative data stored in the storage means to provide a fault diagnosis means for inferring a fault cause, and a notification for notifying the fault cause inferred by the fault diagnosis means. A self-diagnosis and repair system for the image forming apparatus, comprising: a plurality of actuator means capable of changing a function state of the image forming apparatus and eliminating a cause of failure.

<Operation> According to the present invention, the quantitative value input from the data input means is compared with the reference value data and converted into qualitative state data. The status data is compared with the fault diagnosis knowledge. As a result, if the state data does not satisfy the conditions set in the failure diagnosis knowledge, and is relatively low or relatively high, for example, it is determined that a failure symptom has occurred. .

More specifically, the fault diagnosis knowledge sets a condition that a specific parameter must be, for example, normal. If this condition is not satisfied, it is determined that a failure symptom has occurred in the image forming apparatus.

Then, when a failure symptom occurs, the state data is applied to the qualitative data stored in the storage means,
The cause of the resulting failure symptom is inferred, and which of the components of the image forming apparatus is abnormal is output. The output is output using a display device, a sound output device, or the like, as can be understood by a service person or the like.

The serviceman or the like can easily repair the failure by operating the actuator means with reference to the output.

Since the qualitative data is not data unique to a certain image forming apparatus, but data that is common and qualitatively expressed in many image forming apparatuses, the self-diagnosis and repair system according to the present invention includes: It can be easily incorporated into different types of image forming apparatuses.

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

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

The system control circuit 10 includes a digital signal / symbol conversion unit 11, a failure diagnosis unit 12, a failure simulation unit 13,
A target model storage unit 14, a restoration planning unit 15, and a symbol / digital signal conversion unit 16 are included.

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

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

The repair plan unit 15 is a component unit that, when there is a failure, infers a repair plan for repairing the failure and derives a repair operation. 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 used.

A method of inferring a failure diagnosis and a failure simulation, a repair plan, and deriving a repair work in the failure diagnosis unit 12, the failure simulation unit 13, and the repair plan unit 15 will be described later in detail.

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

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

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

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

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

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

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

If it is determined in step S3 that there is a failure,
Inference of the state of the target machine, that is, failure diagnosis and simulation of the failure state are performed (step S4). Specifically, based on the qualitative data stored in the target model storage unit 14 and qualitatively expressing the coupling relationship between the elements constituting the device using parameters, the failure diagnosis unit 12 The caused parameter is retrieved, and the failure simulation section 13 simulates a failure state on the assumption that the retrieved parameter is the cause of the failure. Further, the failure diagnosis unit 12 compares the simulation result with the current parameter value, and diagnoses 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, the failure diagnosis, and the simulation of the failure state, the failure symptoms and the failure cause of the target machine are determined (step S5). Here, the failure symptom refers to the output state of the target machine or the like (for example, in the case of
The cause of the failure is a change in the mechanism or structure of the target machine that causes a change in the symbol (for example, in the case of a copying machine, for example, "a decrease in the light amount of the halogen lamp"). Etc.).

Next, the restoration plan unit 15 infers a restoration method. In this inference, a simulation of the effect of the restoration is also performed (step S6).

Then, a repair plan is determined (step S7), and a control command is developed based on the determined repair plan (step S8). At the time of development into an actual control command, characteristic data unique to the device, such as a limit numerical value for an actuator operation, is read from the target model storage unit 14 and used. The developed control command is converted into an analog signal, applied to the actuator control circuit 5, and the restoration control is executed (step S9). Then, the control ends.

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

Description Using a Specific Target Machine as an Example Before proceeding with a specific description of the target machine, various data and knowledge used in this embodiment will first be summarized and summarized.

The "storage means" of the embodiment stores "characteristic data", "failure diagnosis knowledge", and "qualitative data".

The feature data includes “reference value data”, “operating range data”, and “repair plan knowledge”.

The reference value data is data specific to the target machine, and specifies a qualitative value range for a predetermined parameter. More specifically, the data is data for classifying the relationship between the output range of the sensor or the input quantitative value and the low, normal, and high levels.

The operation range data is data that defines a limit value and the like for the operation of the actuator specific to the device.

The repair plan knowledge is data indicating which physical element of the image forming apparatus should be operated in order to qualitatively change the parameter value of a predetermined parameter.

The failure diagnosis knowledge serves as a reference for determining whether or not the target machine is operating normally. In other words, a specific parameter refers to a setting condition that it must be, for example, normal. Failure diagnosis knowledge includes
"Evaluation function knowledge" is included.

The qualitative data is a qualitative expression of the connection relationship between the elements constituting the apparatus using parameters. “Qualitative” refers to a relationship between A and B, for example, “If A is increased, B is also increased”. The qualitative data includes a “target model”. The target model includes a “substantial model” in Table 1 described later and a “mathematical model” in FIG. The mathematical model is obtained by abstracting the real model and expressing it as a connection tree of each parameter.

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

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

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

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

Also, the expression in FIG. 4 in which the real model is abstracted and represented as a connection tree of each parameter will be referred to as a “mathematical model”.

Then, the “substance model” and the “mathematical model” are collectively referred to as a “target model”. "Object model"
Is qualitative data common to the image forming apparatuses, which is also used for failure repair described later.

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

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

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

In this embodiment, the above-mentioned reference value data can be updated in response to sensing data or a change in the operating state of the machine in a later failure diagnosis or failure repair process.

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

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

 The evaluation function knowledge includes the following knowledge.

_'Here ≦ normal separation performance S _p ≦ normal, O _s, O _s' image density O _s = normal fog degree O _{s if,} S _p is not above conditions,
The target machine is not operating normally.

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

AE sensor value X = 30 the value of the surface potential sensor V _s = 300 densitometer value O _s = 7 Further, the densitometer when using blank original optical density D = 0 the value O _s = fog degree O _{s ′} , The value of the surface potential sensor with the halogen lamp turned off
It is assumed that V _s = dark potential V _n , and their values are respectively fogging degree O _{s ′} = 50 dark potential V _n = 700.

The measurement of these heads of O _s' and the dark potential V _n may be performed by manual operation, when a certain condition, for example, every time the power supply of the objective machine is turned on, or before each start of copying, the sensor May be programmed to be measured automatically by In this embodiment, the latter is adopted.

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

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

In this embodiment, each parameter is symbolized as follows.

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

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

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

Next, each parameter ζ, D _s , V _t , γ _O , V _b , V _s , V _n , which may cause O _s = low, searched in the mathematical model
With respect to X, β, H _L , and D, the failure diagnosis unit 12 detects the cause of the parameter change.

This detection is performed based on the substantial model in Table 1,
In this embodiment, the following failure cause candidates are inferred. That, V _t = low: → transfer transformer failure zeta = low: → paper deterioration V _b = high: → defective gamma _{O =} low developing bias: → deterioration of toner V _n = low: → improper principal charge voltage H _L = High: → Incorrect setting of halogen lamp D = Low: → Document is thin Note that among the parameters, β is the sensitivity of the photoconductor,
It is excluded because it does not rise. Since D _s , V _s and X are represented by other parameters, they are also excluded.

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

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

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

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

As a result, in the case of this example, when the cause of the failure is “halogen lamp setting failure ( _HL = high)”, a result that matches the actual state of the target machine is obtained, and other failures It is inferred that all the cause 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 a defective setting of the halogen lamp. Table 2 shows the state of each parameter of the target machine at that time.

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

Reasoning plan inference Next, the inference of the rehabilitation plan will be described.

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

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

Next, when making inferences with the goal of increasing D _s , V _s
Whether to increase or to lower the V _b, or to obtain any of the conclusions or raising the gamma _O. As described above, the inference is repeated based on the mathematical model, so that a repair operation candidate can be obtained on the mathematical model. The results obtained are as shown in Table 3.

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

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

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

Further, X: logarithm of the amount of reflected light from the document V _s : surface potential of the drum after exposure D _s : toner density on the drum itself cannot be changed; other parameters must be changed indirectly , And is excluded from the repair candidates here.

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

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

On the other hand, the following knowledge is stored in advance in the target model storage unit 14 as repair plan knowledge. That is, (a) raise the V _t → increasing the control voltage of the transfer transformer (b) lowering the V _t → lower the control voltage of the transfer transformer (c) raising the V _n → control voltage of the main charging transformer raising (d) lowering the V _n → lower the control voltage of the principal charge transformer (e) raising the H _L → shifting the halogen lamp control signal to the high voltage side (f) → halogen lamp control to lower the H _L Shift the signal to the lower voltage side.

It is. The repair plan knowledge stored in the target model storage unit 14 is characteristic data unique to this device. By applying the repair plan knowledge to a repair candidate obtained based on a mathematical model, repair operations for increasing O _s include: (a) increasing V _t → increasing the control voltage of the transcription transformer (c ) Raise V _t → Raise control voltage of charging transformer (f) Lower _HL → Shift halogen lamp control signal to lower voltage side.

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

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

Inference of side effects When the three restoration plans derived in the inference of restoration plans are developed on a mathematical model, FIGS. 8 to 13 are obtained. That is, (a) FIGS. 8 and 9 show the case where V _t is increased (FIG. 9 shows _{OS ′} in the case of D = 0 on a mathematical model), (c) When V _n is increased, FIGS. 10 and 11 (FIG. 11 shows _{OS ′} when D = 0 is represented on a mathematical model), (f) _HL decreases 12 and 13 (D = 0 in FIG. 13).
And O _{S 'is} represented by the mathematical model in the case of)
Becomes

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

(1) When increasing the V _t (FIG. 8, FIG. 9) is (a) an output image density increases.

(B) When D = 0, there may be a case where Os _' > normal.
That is, fogging may occur.

(C) S _p > Normal, and separation failure may occur.

(2) If a V _n is raised (FIG. 10, FIG. 11) is (a) an output image density increases.

(B) When D = 0, Os _' > normal, and fog may occur.

(3) When _HL is lowered (FIGS. 12 and 13) (a) Only the output image density is increased without any other side effects.

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

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

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

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

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

However, _if a repair plan to increase Vn is selected, a secondary effect of the possibility of fogging is predicted, so in the mathematical model of FIG. 11,
Which parameter should be operated to lower Os _' is examined based on the mathematical model in FIG. 11, and the operation is selected based on the repair plan knowledge.
As a result, either raise the H _L, or lowering the V _n, or lowers the V _t is selected, repair plan including preventing the fog generating is performed.

That is, as shown in FIG. 14, the inference of the repair operation is developed by assuming a secondary effect. In the inference expansion of the repair operation as shown in FIG. 14, (a) a branch which is inconsistent with the previous repair plan on the mathematical model is not selected.

(B) Select the one that has the least side effect. (C) The one that forms the loop is expanded based on the knowledge that expansion is stopped at that point.

In Figure 14, after _{all, (1) V n ↑ →} H L ↑ → V n ↑ loop, and _{(2) V n ↑ → V} t ↓ → V n ↑ loop, two repair plan remains the.

Now, when performed as a loop repair plan (1), the image density is proper density, i.e. O _s becomes normal. In such a case, since the V _n and H _L are raised, in a state after repair where the image density O _s is returned to normal, the value of the surface potential measured by the sensor 1b as compared to the values that are initially measured Should have changed to something quite high. However, since not repair work is successful, the state of the parameter V _s of the repaired must be symbolized to normal. Therefore, if such, when the repair is completed, based on the measurement value measured by the sensor 1b, reference data for symbolizing the parameter V _s shown in FIG. 5 is changed, for example, 15
It is rewritten with the data shown in the figure.

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

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

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

Further, if the above two remediation plans are not feasible,
Furthermore, the repair plan to increase the V _t of the above-described (1) is selected, and head occurs its side effects, fault diagnosis assuming two defective separation is performed, the repair plan is selected You.

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

Moreover, in the case of this embodiment, as described in the embodiment, O _s repair ends when it becomes the normal is determined,
The repair is stopped in that state.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Of course, an image forming apparatus having only a self-diagnosis system can be configured by excluding the components of the self-repair system.

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

<Effect of the Invention> According to the present invention, when the state of the image forming apparatus is abnormal, the presence or absence of a failure is determined and the cause of the failure is inferred simply by measuring the state data and inputting the data from the data input unit. Is done. Therefore, the serviceman or the like can easily repair the failure of the image forming apparatus only by selecting and operating the actuator means so as to remove the inferred failure cause.

As described above, according to the present invention, an image forming apparatus having a system capable of performing a semi-automated self-diagnosis in which the cause of a failure is diagnosed and performing a repair based on the self-diagnosis by simply inputting data when a failure is considered to occur Can be provided.

Further, according to the present invention, since the cause of the failure is based on qualitative data common to the image forming apparatuses, the present invention has a self-diagnosis and repair system that can handle unknown failures that are not explicitly described. An image forming apparatus can be provided.

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

[Brief description of the drawings]

FIG. 1 is a block diagram showing a 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 showing a mathematical model of this embodiment. FIG. 5 is a diagram showing reference value data of each parameter necessary for symbolizing each parameter. FIG. 6 and FIG. 7 are diagrams showing development for failure diagnosis on a mathematical model. FIG. 8 to FIG. 13 are views showing developments for inference of side effects on a mathematical model. FIG. 14 is a diagram showing an operation when selecting a repair plan. FIG. 15 is a diagram illustrating the updated reference value data. In the figure, 1a, 1b, 1c: sensor, 6a, 6b, 6c: actuator, 10: system control circuit, 11: digital signal / symbol converter, 12: fault diagnosis unit, 13: fault simulation Unit, 14 ... Target model storage unit, 15 ...
Restoration Planning Department, 16 Symbol / Digital Signal Converter,
Is shown.

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Inventor Yoshifumi Ishii 1-2-28 Tamatsukuri, Chuo-ku, Osaka-shi, Osaka Prefecture Inside Mita Kogyo Co., Ltd. (72) Inventor Hiroshi Kusumoto 1-2-2 Tamatsukuri, Chuo-ku, Osaka-shi, Osaka No. 28 Mita Kogyo Co., Ltd. (56) References JP-A-63-106810 (JP, A) JP-A-63-129405 (JP, A) JP-A-63-182710 (JP, A) JP-A-1- 284905 (JP, A) JP-A-2-2406 (JP, A) JP-A-2-42535 (JP, A) JP-A-2-155006 (JP, A) JP-A-3-68002 (JP, A) JP-A-2-162348 (JP, A) JP-A-2-71280 (JP, A) JP-A-61-36780 (JP, A) JP-A-63-225253 (JP, A) JP-A-2-20879 (JP, A) JP-A-3-27058 (JP, A) JP-A-58-221856 (JP, A) JP-A-63-266263 (JP, A) JP-A-58-72165 (JP, A) JP-A-64-81617 (JP, A) JP-A-2-37368 (JP, A) JP-A-62-90669 (JP, A) JP, A) JP-A-1-276175 (JP, A) JP-A-60-20876 (JP, A) JP-A-62-235916 (JP, A) JP-A-2-235074 (JP, A) JP Japanese Patent Application Laid-Open No. Hei 1-253376 (JP, A) Japanese Patent Laid-open No. Hei 1-253376 (JP, A) Japanese Patent Laid-open No. Sho 58-94012 (JP, A) Japanese Patent Laid-Open No. Hei 1-274209 (JP, A) , B2)

Claims (1)

(57) [Claims] A self-diagnosis and repair system for an image forming apparatus that embodies image data and generates a viewable image, wherein the self-diagnosis and restoration system qualitatively expresses the image forming apparatus using a plurality of parameters. Data, reference value data for setting a qualitative value category for a predetermined parameter among the parameters, and fault diagnosis knowledge for setting a condition that can take the parameter for a specific parameter among the parameters are stored. Storage means, data input means for inputting a quantitative value indicating a functional state at a predetermined location of the image forming apparatus, and comparing the quantitative value input from the data input means with reference value data stored in the storage means Means for converting the data into qualitative state data. The converted state data is stored in the storage means. Failure determination means for determining whether the image forming apparatus is normal or has a failure symptom, in response to the failure determination means determining that the image forming apparatus has a failure symptom. A failure diagnosis unit for inferring a failure cause by applying the state data to the qualitative data stored in the storage unit, a notification unit for reporting the failure cause inferred by the failure diagnosis unit, and a function of the image forming apparatus. A self-diagnosis and repair system for an image forming apparatus, comprising: a plurality of actuator means capable of changing a state and eliminating a cause of a failure.