JP2534389B2 - Self-diagnosis and self-healing system for image forming apparatus - Google Patents

Description

Description: <Industrial field of application> The present invention relates to a self-diagnosis system for an image forming apparatus. More specifically, the present invention relates to an apparatus and a system that self-diagnose the operating state of an image forming apparatus using artificial intelligence and knowledge engineering, which have been actively researched in recent years.

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

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

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

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

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

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

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

There were problems such as.

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

The present invention has been made against such a background of the prior art, and an object of the present invention is to provide a novel self-diagnosis system for an image forming apparatus which solves the drawbacks of the prior art.

<Means for Solving the Problems> The present invention is a self-diagnosis and self-repair system for an image forming apparatus that embodies image data and generates a visually recognizable image. Qualitatively expressed by using, reference value data for specifying a qualitative value classification for a predetermined parameter among the parameters, and a specific parameter among the parameters, the conditions that the parameter should take Failure diagnosis knowledge for setting, storage means storing therein, data input means for inputting a quantitative value indicating a functional state at a predetermined position of the image forming apparatus, and storing the quantitative value input from the data input means By comparing with the reference value data stored in the means,
Conversion means for converting to qualitative status data, comparing the status data converted by the conversion means with failure diagnosis knowledge stored in the storage means to determine whether the image forming apparatus is normal or has a failure symptom. Failure determination means for determining, In response to the failure determination means determining that a failure has occurred in the image forming apparatus, the state data is applied to the qualitative data stored in the storage means to infer the cause of the failure. Failure diagnosis means, and a predetermined repair procedure is manually applied to the image forming apparatus so that the cause of the failure inferred by the failure diagnosis means is removed, and then the image formation after the repair procedure is applied. The functional state at a predetermined location of the device is measured, the measured quantitative value is input from the data input means, the quantitative value is converted into qualitative state data, and the failure diagnosis is performed. As a result of being compared with the knowledge, in response to the failure determination means determining that the image forming apparatus is normal, the failure determination means stores it in the storage means based on the quantitative value input from the data input means. A self-diagnosis and self-repair system for an image forming apparatus, comprising: reference value data updating means for updating reference value data.

<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. Each state data is compared with the fault diagnosis knowledge. As a result, it is determined that a failure symptom is occurring when, for example, a certain state data does not satisfy the condition set in the failure diagnosis knowledge and is, for example, relatively low or relatively high. .

Then, when a failure symptom occurs, the state data is applied to the qualitative data stored in the storage unit to infer the cause of the failure that has occurred, and any one of the components of the image forming apparatus is inferred. Outputs whether the element is abnormal.

A serviceman or the like refers to this output to remove the cause of the failure of the image forming apparatus, and performs repair processing on the image forming apparatus so as to eliminate the occurrence of failure symptoms. Then, after the repairing treatment is performed, the functional state at a predetermined portion of the image forming apparatus is measured. Then, the measured quantitative value is input from the data input means.

The quantitative value input from the data input means is compared with the reference value data and converted into qualitative state data in the same manner as described above, and the converted state data is compared with the failure diagnosis knowledge to determine the image forming apparatus. It is determined whether normal or a failure symptom has occurred. At this time, in response to the failure determining unit determining that the image forming apparatus is normal, the reference value data updating unit stores the reference value data updating unit in the storage unit based on the quantitative value input from the data input unit at this time. Update the reference value data.

The reason for updating the reference value data is as follows. When the image forming apparatus is repaired by a service person or the like and the failure symptom does not occur, the image forming apparatus can be said to be normal. Therefore, the quantitative value indicating the functional state at a predetermined portion of the image forming apparatus at that time must be symbolized as "normal" which means that the apparatus is normal. By the way, the quantitative value indicating the functional state in the predetermined portion of the image forming apparatus after the repairing treatment is
It may be different from that before restoration. therefore,
Reference value for updating the reference value data for symbolizing the quantitative value input from the data input means, and the reference value data always properly symbolizes the quantitative value indicating the functional state of the device after failure repair. You need to keep it in the data.

Since the qualitative data is not data specific to a certain image forming apparatus but is data that is common and qualitatively expressed in many image forming apparatuses, the self-diagnosis system according to the present invention has different types. Can be easily incorporated into the image forming apparatus.

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

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

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

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.

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

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

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

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

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

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

That is, sensor 1a: output less than ka ₁ = low output ka _{1 to} ka ₂ = normal output ka ₂ exceeds = high sensor 1b: output less than kb ₁ = low output kb _{1 to} ka ₂ = normal output kb ₂ exceeds = high Sensor 1c: Output less than kc ₁ = Low output kc _{1 to} ka ₂ = Normal output Exceed kc ₂ = High. Digital signal / symbol converter 11
Then, based on the reference value data specific to the target machine set in the target model storage unit 14, the digital signals from the sensors 1a to 1c are converted into symbols of "low", "normal" or "high". .

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

If it is determined that there is a failure in step S3,
Inference of the state of the target machine, that is, failure diagnosis and simulation of the failure state are performed (step S4). Specifically, 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, in the failure diagnosis unit 12, the simulation result and the current parameter value are compared to judge the correctness of the assumption that the retrieved parameter is the cause of the failure. The above processing is performed on a plurality of parameters to be searched.

As a result of the failure 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 the change in the symbol (for example, in the case of a copying machine, "low light intensity of halogen lamp"). Etc.).

Next, the restoration plan unit 15 infers a restoration method. In this inference, a simulation of the effect of 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 a specific example. 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 starting a specific description of the target machine, various data and knowledge used in this embodiment will first be summarized and summarized.

The various data and knowledge are stored in the storage unit or set in the adjustment range setting unit. The storage means and the adjustment range setting means are specifically the "target model storage section 14" in FIG.

The stored or set data and the like include “feature data”, “fault diagnosis knowledge”, and “qualitative data”.

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

The “reference value data” is data specific to the target machine, and identifies a qualitative value category regarding a predetermined parameter. More specifically, it is data that distinguishes the output range of the sensor and the relationship between low, normal, and high.

The "operating range data" is data that sets a predetermined adjustment range for a predetermined actuator means among the plurality of actuator means. In other words, it is data that defines a limit numerical value or the like for the operation of the actuator peculiar to the device.

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

“Qualitative data” qualitatively expresses the connection relationship between the elements constituting the device 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 the “substantial model” of Table 1 and the “mathematical model” of FIG. 4 described later. 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 the photosensitive drum and the copy sheet, are provided as actuators.

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

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

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

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

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

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

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

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

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

 The evaluation function knowledge includes the following knowledge.

Image density O _s = normal Fogging degree OS _′ ≦ normal Separation performance S _P ≦ normal Where O _s , OS _′ , and SP do not _{satisfy the} 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 Surface potential sensor value V _s = 300 Densitometer value O _s = 7 Also, densitometer value when using a blank original with optical density D = 0
O _s = fogging degree O _{s ′} , value of surface potential sensor with halogen lamp off
It is assumed that V _s = dark potential V _n , and their values are fog degree O _{s ′} = 50 and dark potential V _n = 700, respectively.

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,} O S ', V n , respectively,
It is converted into a symbol in the digital signal / symbol converter 11.

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

In this embodiment, each parameter is symbolized as follows.

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

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 ₀ , V _b , V that may cause the retrieved O _s = low in the mathematical model.
_With respect to _s , V _n , 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 is, V _t = low: → transfer transformer failure zeta = low: → paper deterioration V _b = high: → defective gamma ₀ = low developing bias: → deterioration of toner V _n = low: → principal charge voltage Defective _HL = High: → Halogen lamp setting error D = Low: → Original is thin Note that β is the sensitivity of the photoconductor.
It is excluded because it does not rise. D _s , V _s and X are also excluded because they are represented by other parameters.

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

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

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

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

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

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

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

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
One can either conclude either increasing, decreasing V _b , or increasing γ ₀ . In this way, by repeating the inference based on the mathematical model, candidates for the repair operation can be obtained on the mathematical model. The results obtained are 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 ₀ : Toner sensitivity cannot be changed, and ζ: Paper sensitivity cannot be changed.

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

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

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

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

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

Is. The repair plan knowledge stored in the target model storage unit 14 is characteristic data unique to this device. The restoration plan knowledge is data representing which physical element of the image forming apparatus should be operated in order to qualitatively change the parameter value for the predetermined parameter as described above.
Therefore, by applying the repair plan knowledge to a repair candidate obtained based on a mathematical model, as a repair operation for increasing O _s , (a) increase V _t → increase the control voltage of the transcription transformer. There are three methods of (c) increasing V _n → increasing the control voltage of the charging transformer (f) decreasing H _L → shifting the halogen lamp control signal to the low voltage side.

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

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

Inference of secondary effects When the three restoration plans derived in the inference of restoration plans are developed on a mathematical model, Figs. 8 to 13 are obtained. That is, (a) when V _t is increased, FIG. 8 and FIG. 9 (in FIG. 9, O _{s ′} when D = 0 is represented on the mathematical model), (c) When V _n is increased, Fig. 10 and Fig. 11 (O _s' when D = 0 is shown in the mathematical model in Fig. 11), (f) _HL is decreased 12 and 13 (FIG. 13 shows D = 0 when
(O _s'in the case of
Becomes

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

That is, (1) When V _t is increased (FIGS. 8 and 9) (a) The output image density is increased.

(B) When D = 0, O _{s ′} > normal in some cases. That is, fogging may occur.

(C) S _P > Normal, and there is a possibility that separation failure will occur.

(2) When V _n is increased (Figs. 10 and 11) (a) The output image density increases.

(B) When D = 0, O _{s ′} > normal, and fog may occur.

(3) When H _L is lowered (FIGS. 12 and 13) (a) The output image density only increases, and there are no other secondary effects.

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

In this embodiment, regarding the side effect, it is determined that the remediation plan having a small number of side effects is a repair plan having a small side effect. The reason for this is that in the system described in this embodiment, the contents of the secondary influence cannot be taken into consideration. That is, when a side effect occurs, it cannot be determined whether the side effect is fatal or non-fatal. Therefore, we decided to simply consider the thing that is unlikely to have a side effect as having a small side effect.

Note that the secondary effect mentioned here means that a parameter that does not satisfy the condition set in the evaluation function knowledge may be generated as a result of the restoration plan.

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

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

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

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

However, _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, _HL is increased, V _n is decreased, or V _t is decreased, and a repair plan including prevention of fogging 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) the branch that is inconsistent with the previous repair plan on the mathematical model is not selected (b) the one with the least side effect is selected (c) ) The expansion that is based on the knowledge that the one that forms the loop stops the expansion at that point.

In Fig. 14, after all, two repair plans remain: (1) V _n ↑ → H _L ↑ → V _n ↑ loop, and (2) V _n ↑ → V _t ↓ → V _n ↑ loop.

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

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 above-mentioned repair plan of increasing v _{t in} (1) is selected, fault diagnosis is performed assuming two factors that are its secondary effects, fogging occurrence and separation failure, and a repair plan is selected. It

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

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

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

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

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

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

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

In the above case, if for the first selected repair plan it was determined that the side effects simulated could not be eliminated by activating other actuator means, simulating the side effects simulated. May occur if the first repair plan is abandoned, then the second repair plan is taken up, and the actuator means selected based on the second repair plan is activated. The side effect can be removed by simulating the side effect and determining whether or not the simulated side effect can be removed by operating an actuator means other than the actuator means. At times, repair work is performed based on the second repair plan.

In this way, when the first repair plan is extracted from the plurality of repair plans derived in the inference of the repair plan, the secondary effect in that case is inferred, and the secondary effect can be eliminated. Then, 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-mentioned specific example, although the operating range of each actuator is not particularly mentioned, the operating range for setting the operating range of the actuator in the characteristic data unique to the device stored in the target model storage unit 14. If data is included, it can be determined that the actuator can be operated when the output state of the actuator is within the stored operating range, and when the output state of the actuator reaches the upper or lower limit of the stored operating range. , It is possible to determine that the actuator cannot be operated and use it for determining whether or not the repair work is correct.

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

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

In addition, based on the result of the self-diagnosis performed by the device, if a system is constructed in which only the actuator for repairing the failure is selected and the actuator to be operated is displayed without actually operating the actuator, the serviceman The image forming apparatus can be provided with a non-autonomous repair system that requires only operating the displayed actuator.

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

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

<Effects of the Invention> According to the present invention, when the state of the image forming apparatus is abnormal, the presence or absence of the failure is determined only by measuring the status data and inputting the status data from the data input means. In addition, it is possible to provide an image forming apparatus capable of accurately inferring and outputting the cause of the failure.

Therefore, a service person or the like can quickly perform repair based on the output cause of failure. That is, the actuator or the like of the image forming apparatus may be operated according to the instruction, or the broken part may be replaced.

Further, according to the present invention, since the cause of the failure is based on the qualitative data common to the image forming apparatus, the image forming having the failure diagnosis system capable of handling the unknown failure not explicitly described. The device can be taken.

Furthermore, the failure diagnosis system according to the present invention can be applied not only to a specific image forming apparatus but to many types of image forming apparatuses in common, and as a result, the failure diagnosis is inexpensive. An image forming apparatus having a system can be provided.

[Brief description of drawings]

FIG. 1 is a block diagram showing a system configuration of an embodiment of the present invention. FIG. 2 is a flow chart showing the operation of the control circuit in FIG. FIG. 3 is a diagram showing a schematic configuration of the present invention when used in a plain paper copying machine. FIG. 4 is a diagram showing a mathematical model of this embodiment. FIG. 5 is a diagram showing reference value data of each parameter necessary for symbolizing each parameter. FIG. 6 and FIG. 7 are diagrams showing development for failure diagnosis on a mathematical model. FIG. 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,
Indicates.

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification number Internal reference number FI Technical indication location G03G 15/08 501 501-716H H05B 23/02 302Y 15/16 G03G 15/04 120 G05B 23/02 302 (72) Inventor Yoshifumi Ishii 1-22 Tamatsukuri, Chuo-ku, Osaka City, Osaka Prefecture Mita Industry Co., Ltd. (72) Hiroshi Kusumoto 1-2-2 Tamatsukuri, Chuo-ku, Osaka City, Osaka Mita Industry Co., Ltd. (56) Reference JP-A-63-106810 (JP, A) JP-A-63-129405 (JP, A) JP-A-63-182710 (JP, A) JP-A-1-274209 (JP, A) JP-A-1-284905 (JP, A) JP-A-2-2406 (JP, A) JP-A-2-42535 (JP, A) JP-A-2-155006 (JP, A) JP-A-63-142370 (JP, A) JP-A-3-68002 JP, A) JP 2-162348 (JP, A) JP 2-71280 (JP, A) JP 61-36780 (JP, A) JP 63-225253 (JP, A) JP Japanese Patent Laid-Open No. 2-20879 (JP, A) Japanese Patent Laid-Open No. 3-27058 (JP, A) Japanese Patent Laid-Open No. 58-221856 (JP, A) Japanese Patent Laid-Open No. 63-262663 (JP, A) Japanese Patent Laid-Open No. 58-72165 (JP , A) JP 64-81617 (JP, A) JP 2-37368 (JP, A) JP 62-90669 (JP, A) JP 1-276175 (JP, A) JP 60-208768 (JP, A) JP-A-62-35916 (JP, A) JP-A-2-235074 (JP, A) JP-A 1-253764 (JP, A) JP-A 1-253763 (JP, A) A) JP 58-94012 (JP, A) JP-B 7-48180 (JP, B2)

Claims (1)

(57) [Claims] 1. A self-diagnosis and self-repair system for an image forming apparatus that embodies image data to generate a visible image, wherein the image forming apparatus is qualitatively represented using a plurality of parameters. Qualitative data, reference value data that identifies a qualitative value classification for a predetermined parameter among the parameters, and fault diagnosis knowledge that sets conditions that the parameter should take for the specific parameter among the parameters, Stored storage means, data input means for inputting a quantitative value indicating a functional state at a predetermined location of the image forming apparatus, reference value data stored in the storing means for the quantitative value input from the data input means Conversion means for converting into qualitative status data by comparing with the status data converted by the conversion means. 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. In response to the determination that the status is determined to be present, the status data is applied to the qualitative data stored in the storage means to remove the failure cause inferred by the failure diagnosis means for inferring the failure cause. As described above, after the predetermined repair treatment is manually applied to the image forming apparatus, the functional state at the predetermined portion of the image forming apparatus after the repair treatment is performed is measured, and the measured quantitative value is By inputting from the data input means, converting the quantitative value into qualitative state data and comparing with the failure diagnosis knowledge, the failure determination means makes the image forming apparatus normal. In response to the determination, the image including a reference value data updating unit that updates the reference value data stored in the storage unit based on the quantitative value input from the data input unit. Self-diagnosis and self-healing system for forming equipment.