JPH05273807A - Self-diagnosable image forming device - Google Patents

Abstract

PURPOSE:To realize accurate diagnosis for a fault as to a self-diagnosable system for an image forming device. CONSTITUTION:A pseudo fault occurrence part 14 operates actuator controllers 2C, 3C, and 5C, then the fault is forcibly caused on the image forming device. The detection values of sensors X, VS, and DS at the time of forcibly causing the fault are read, and membership function stored in a membership function forming part 13 is corrected based on the detection values read and the detection values obtained from the sensors X, VS, and DS before the fault is caused. Then, faulty symptoms is caused on the device, and the detection values of the sensors X, VS, and DS are converted to a fuzzy qualitative values based on the corrected membership function. And the fuzzy qualitative values are compared with the set of the qualitative values outputted from an outputting part 12, so that the fault is estimated.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image forming apparatus having a self-diagnosis system. More specifically, the present invention relates to an image forming apparatus that enables self-diagnosis of an operating state of the apparatus by utilizing artificial intelligence and knowledge engineering, which have been actively researched in recent years.

[0002]

2. Description of the Related Art In the field of development of precision machinery and industrial machinery, experts who have recently used artificial intelligence (so-called AI) technology in order to save labor in maintenance work and prolong automatic operation. Research on the system is being actively conducted. Some expert systems self-diagnose whether or not a failure has occurred in the device and also self-repair the failure that has occurred.

However, since the conventional expert system (automatic adjustment system or failure diagnosis system) basically only operates the corresponding actuator based on the output of a certain sensor, it is a self-repairing machine. It wasn't perfect. Therefore, the applicant of the present application has found a machine control method using diagnosis / repair inference on a target model based on qualitative physics, and using such a machine control method, novel self-diagnosis and self-diagnosis for an image forming apparatus are performed. He invented a restoration system and filed a patent application (see, for example, Japanese Patent Application No. 2-252191).

The self-diagnosis and self-repair system for the image forming apparatus according to this prior application has the following features. That is, (1) The detection value of the sensor provided in the target machine (image forming apparatus) is converted into a qualitative value and used for control. (2) The structure and characteristics of the image forming apparatus are qualitatively expressed using a causal relationship network (parameter model) of parameters representing the characteristics of the image forming apparatus.

(3) The sensor value converted into a qualitative value is applied to a parameter model to perform a qualitative simulation for failure diagnosis and failure repair inference. In other words, a qualitative model-based system (Qualitative Mo
It is to perform fault diagnosis and fault repair by the del Based System (QMS).

According to the self-diagnosis and self-repair system of the applicant of the present application having the above characteristics, even if a failure such as a structural change occurs in the image forming apparatus, it is flexible. Is available. This is because it is possible to dynamically change the control point and control loop of the target machine by using the qualitative simulation.

[0007]

However, in the self-diagnosis and self-repair system according to the above-mentioned prior application,
When converting the detected value of the sensor into a qualitative value, there was a possibility that an error would occur in the conversion. This is because, when converting the sensor detection value into a qualitative value, a boundary marker is defined in the qualitative space and it is converted into a different qualitative value depending on whether the detected value is larger or smaller than the boundary marker. Therefore, the boundary mark must be properly defined.

However, this boundary mark may change depending on the environment in which the image forming apparatus is used, and the sensor detection value itself may not always be an accurate value due to the limit of sensor measurement accuracy. .. Therefore, in the conventional system, the qualitative quantification of the sensor detection value, which is the basis of the control, varies, and as a result, an accurate qualitative simulation cannot be performed, and an error may occur in failure diagnosis or failure repair. ..

Therefore, an object of the present invention is to provide an image forming apparatus which enables accurate failure diagnosis.

[0010]

The invention according to claim 1 is
An image forming apparatus capable of self-diagnosing a failure occurring in the apparatus, the output means outputting a set of qualitative values of parameters representing the failure state of the apparatus, and detecting the states of a plurality of predetermined parts of the apparatus. Boundary mark of the qualitative quantity space required for converting the detected value of each sensor into a qualitative value, which is provided as a membership function of the fuzzy theory. Mark storage means and the detection values of the plurality of sensors that are read before the failure is forcedly caused in the device and before the failure, and the detection values of the plurality of sensors that are read when the failure is caused. By using the boundary marker correction means for correcting the membership function stored in the boundary marker storage means, and when a failure symptom appears in the device, the detection values of the plurality of sensors are read. A conversion means for converting the detection value of each sensor into a fuzzy qualitative value using the membership function stored in the boundary mark storage means, and the fuzzy qualitative value converted by the conversion means and output from the output means. Selecting means for selecting a set of qualitative values having a predetermined relationship with the fuzzy qualitative value in order to identify the fault causing the manifesting fault symptom. It is characterized by.

According to a second aspect of the present invention, the image forming apparatus further includes a fault repairing unit that operates to repair a fault set in the set of qualitative values selected by the selecting unit. It is a feature. Claim 3
In the image forming apparatus according to the invention, the timing for forcibly causing a failure in the apparatus in order to correct the membership function by the boundary marker correcting means is determined by the time when the failure repairing is completed by the failure repairing means. It is characterized by being.

According to a fourth aspect of the present invention, in the image forming apparatus, the boundary mark correcting means corrects the membership function, so that the timing for forcibly causing a failure in the apparatus is a correction request signal by a manual operation or the like. It is characterized in that when is input. According to a fifth aspect of the present invention, in the image forming apparatus, the membership functions stored in the boundary mark storage means are stored for each failure symptom.

According to a sixth aspect of the present invention, in the image forming apparatus, the selecting means defines a qualitative amount space, and the set of qualitative values and the fuzzy qualitative value are expressed in a positional relationship within the qualitative amount space. It is characterized by comparing and selecting a set of qualitative values closest to the position of the fuzzy qualitative value. According to a seventh aspect of the present invention, the output unit qualitatively represents the image forming apparatus by a causal relationship network of parameters representing the characteristics of the apparatus, and when it is assumed that a failure symptom appears in the apparatus, the apparatus Is characterized by qualitatively simulating all possible states using the causal relationship network of the parameters and outputting the results.

[0014]

According to the invention described in claim 1, when a failure symptom appears in the device, the detection values of a plurality of sensors are read,
Each detected value is converted into a fuzzy qualitative value. The membership function as a boundary marker required when converting the detected value of the sensor into a fuzzy qualitative value is modified by the pseudo-fault method of forcibly causing a failure in the device. Therefore, the membership function required for the conversion to the fuzzy qualitative value can be constantly corrected to the optimum value, and the sensor detection value can be accurately converted to the fuzzy qualitative value. The converted fuzzy qualitative value is compared with a set of qualitative values, and a set of qualitative values having a predetermined relationship with the fuzzy qualitative value, for example, the closest relationship with the fuzzy qualitative value is selected. Then, it is estimated that the failure set in the selected set of qualitative values is the cause of this failure symptom.

According to the second aspect of the invention, the estimated failure is automatically repaired. According to the invention as set forth in claim 3, since the timing for correcting the membership function is the degree of completion of the failure repair, even if the parameter of the apparatus is changed due to the failure repair and the boundary mark is changed, the change is also made. The membership function can be modified by following.

According to the fourth aspect of the present invention, the membership function is corrected by the pseudo-fault method every time a correction request signal is manually input by a service person or the like. The function can be modified. According to the invention of claim 5, since the membership function is stored for each failure symptom, the detection value of the sensor can be more accurately converted into the fuzzy qualitative value for each failure symptom.

According to the sixth aspect of the present invention, when comparing the fuzzy qualitative value and the set of qualitative values, the qualitative amount space is defined, and the qualitative value set is selected according to the positional relationship in the qualitative amount space. It When a qualitative value set is selected based on the positional relationship in the qualitative amount space, there is no variation in the selection of the qualitative value set, and the selection accuracy is improved. According to the method invention of claim 7, the set of qualitative values can be organized for each failure in a state where it is easy to use.

[0018]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A self-diagnosis and self-repair system applied to a small electrophotographic copying machine will be described below as an embodiment with reference to the drawings. FIG. 1 is a mechanical configuration diagram of a small-sized electrophotographic copying machine to which the present invention is applied, and is a diagram schematically showing only a portion related to the present invention. Figure 1
, 1 is the photoconductor drum, 2 is the main charger,
Reference numeral 3 is a halogen lamp for illuminating the original, 4 is a developing device, and 5 is a transfer / separation charger.

The main charger 2 is connected to a main charger controller 2C for changing the discharge voltage of the main charger. Further, a halogen light amount controller 3C for controlling the light amount of the halogen lamp 3 is connected to the halogen lamp 3. further,
The transfer / separation charger 5 includes a transfer charger controller 5 for controlling the discharge voltage of the charger 5, that is, the transfer voltage between the photosensitive drum 1 and the copy sheet.
C is connected.

In an electrophotographic copying machine, it is of utmost importance whether the obtained copy image is beautifully finished (normal). Therefore, this embodiment automatically detects whether the obtained copy image is normal, has image fog, or has a light image, and is applicable when the obtained copy has image fog or is light. The cause of the symptom,
That is, an apparatus will be described as an example for identifying a failure and self-repairing the failure.

In this embodiment, for example, four sensors are provided. That is, a light amount sensor X for measuring the amount of light for exposing the photosensitive drum 1 (in other words, the amount of light of the halogen lamp 3), a surface potential sensor Vs for measuring the surface potential of the exposed photosensitive drum 1, Photoconductor drum 1
Toner concentration sensor D for detecting the upper toner concentration
s and the copy density sensor Os. The copy density sensor Os is for detecting the density of the copy image formed by this electrophotographic copying machine. Based on the detection output Os of the copy density sensor Os, it is determined whether the electrophotographic copying machine is normal, image fog has occurred as a failure symptom, or the image is light.

FIG. 2 is a functional block diagram of the small-sized electrophotographic copying machine shown in FIG. 1, and shows only the portion related to the present invention. In FIG. 2, a block with rounded corners represents a function by so-called hardware, and a block with a sharp corner represents a function by so-called software (a program process executed in a computer). Note that the division of functions by hardware and functions by software is an example, and the functions of software may be realized by hardware.

The correspondence relationship between the functional blocks of FIG. 2 and the mechanical configuration of FIG. 1 is as follows. That is, the sensor of FIG. 2 includes the light quantity sensor X, the surface potential sensor Vs, and
A toner density sensor Ds and a copy density sensor Os are included. The actuator controller of FIG. 2 includes the main charger controller 2C, the halogen light amount controller 3C and the transfer charger controller 5C of FIG. The actuator of FIG.
The main charger 2, the halogen lamp 3 and the transfer / separation charger 5 of FIG. 1 are included.

In FIG. 2, the functional block by software is divided into, for example, four functional blocks.
That is, the diagnosis / repair inference unit 11, the output unit 12, the membership function generation unit 13, and the pseudo failure generation unit 14
Is. The output unit 12 generates and outputs a set of qualitative values of characteristic parameters (hereinafter, simply referred to as a “set of qualitative values”) representing the failure states of the devices illustrated in Tables 1 and 2 by qualitative simulation. To do. Here, a set of qualitative values is a qualitative simulation of the device state when a failure occurs under the condition that the failure that occurs at one time in the electrophotographic copying machine is a single failure. This is a possible condition for our device.

[0025]

[Table 1]

[0026]

[Table 2]

Generation of a set of qualitative values can be performed using the qualitative simulation described in the above-mentioned applicant's earlier application (Japanese Patent Application No. 2-252191). The method of qualitative simulation is briefly described as follows. This electrophotographic copying machine is expressed as a combination of a plurality of elements by grasping it from a physical fulcrum, and the behavior and attributes of each element and the connection relationship between each element are qualitatively expressed using parameters. The parameter model shown in is obtained. The parameter model shown in FIG. 3 is a simplified model in which only parameters related to the copy density parameter Os are taken out.

In the parameter model shown in FIG. 3, H
l is the light amount parameter of the halogen lamp 3, D is the optical density parameter of the document, X is the light amount parameter for exposing the photosensitive drum 1, and β is the sensitivity parameter of the photosensitive drum 1.
Vn is a surface potential parameter of the photosensitive drum 1 after main charging, Vs is a surface potential parameter of the photosensitive drum 1 after exposure, Vb is a developing bias parameter, γ ₀ is a toner sensitivity parameter, and Ds is a drum sensitivity parameter. An image density (toner density) parameter, Vt is a transfer voltage parameter, and ζ is a paper sensitivity parameter. Among these parameters, D, β, γ _0, and ζ are unlikely to change, and can be regarded as fixed values. Therefore, the reason why the copy density parameter Os changes is H
It can be inferred that this is due to a change in any one of l, Vn, Vb, and Vt. When these four parameters Hl, Vn, Vb or Vt change and Os changes, the change inevitably changes the three sense target parameters X, Vs or Ds (circled in FIG. 3) ( However, only when it is caused by the change in Vt,
X, Vs, and Ds do not change).

The qualitative simulation for generating the set of qualitative values is based on the assumption that the electrophotographic copying machine has a single failure at one time, as described above. Therefore, the sense target parameters X and Vs are determined for each of the Hl (halogen lamp) failure, the Vn (main charger) failure, the Vb (development bias) failure, and the Vt (transfer charger) failure. , D
The state of s is different. Therefore, this state is inferred and output as a set of qualitative values.

Next, a specific example of how to generate a set of qualitative values will be described using the parameter model of FIG. It is assumed that the copy density becomes abnormal and Os becomes high (+). If the cause of Os: high (+) is Hl: low (-), X becomes low (-). If the cause of Os: high (+) is due to the change of Vn, Vb, or Vt,
X is normal (N). This is because the generation of a set of qualitative values is based on the premise that a failure that occurs at one time in the electrophotographic copying machine is a single failure. Thus, for Os: high (+), X must be normal (N) or high (+) and cannot be low (-).

On the other hand, the root cause of Os: high (+) is Hl.
If so, Hl: low (-), and changes in Hl should affect X, Vs, and Ds on the parametric model. This is because if Hl is changed to such an extent that it has no influence, Os will not change as a result. Therefore, if Hl is the root cause that causes the failure symptom, that is, if it is a failure, X, Vs, and Ds
Cannot be normal (N).

As described above, the set of qualitative values limits the cause when the copy density parameter Os shows an abnormality to the change of the single parameter, and the change of the parameter is the sense target parameter (in FIG. 3). It is generated assuming that the parameter (encircled) is always affected. When a set of qualitative values is generated under such conditions, only those that can actually occur in the device are obtained as the set of qualitative values.

Specific examples of qualitative value sets are shown in Tables 1 and 2. Table 1 shows a set of four qualitative values when the failure symptom "image fog" occurs in this electrophotographic copying machine.
Table 1 is obtained as follows. When image fogging occurs in the image copied by the electrophotographic copying machine, from the parameter model of FIG.
Hl (halogen lamp) failure, Vn (main charger) failure, Vb (development bias) failure, or Vt
(Transfer charger) Defective can be inferred.

In this case, if the above-mentioned failure is limited to a single failure, and if the change in Hl necessarily affects other parameters, two conditions are applied. In this case, the parameter X should be low (-), the parameter Vs should be high (+), and the parameter Ds should be high (+), and no other state is taken.

If the cause of the image fog is Vn defect, the parameter X should be normal (N), the parameter Vs should be high (+), and the parameter Ds should be high (+). I will not take it. When the cause of image fogging is Vb defect, the parameter X is normal (N),
The parameter Vs should be normal (N) and the parameter Ds should be high (+), and no other state is taken.

When the cause of the image fog is Vt defect, the parameter X should be normal (N), the parameter Vs should be normal (N), and the parameter Ds should be normal (N). I will not take it. The numerical value of "1.0" added to each parameter state in Table 1 indicates the degree in the membership function of the fuzzy theory described later. The advantages of introducing the membership function of this fuzzy theory will be described later.

Similarly, when the density of the copy obtained by this electrophotographic copying machine is low, the causes are the Hl (halogen lamp) defect, Vn (main charger) defect, Vb (development bias) according to the parameter model of FIG. ) Defects or Vt (transfer charger) defects are estimated, and sense target parameters X, Vs, and D when the respective defects occur
The state of s is as shown in Table 2.

The set of qualitative values exemplified in Tables 1 and 2 above is
If necessary, the output unit 12 obtains a qualitative simulation and outputs it. The set of qualitative values also includes
The inferred repair method may be included for each failure in each failure symptom. For example, a repair method as shown in the following Table 3 can be included for the failure "Hl defect" of the failure symptom "image fog". This restoration method is also required by qualitative simulation.

[0039]

[Table 3]

Next, the membership function generator 1 of FIG.
As illustrated in FIGS. 4 and 5, a membership function 3 is stored for each failure symptom, which is used for qualifying the detection values of the light amount sensor X, the surface potential sensor Vs, and the toner concentration sensor Ds. ing. As is well known, the membership function is a function that defines the degree (grade) of a certain element belonging to a certain set in the fuzzy theory.

For example, FIG. 4 shows a failure symptom "image fogging".
The membership functions of X, Vs, and Ds used at times are shown. When the diagnosis / repair reasoning unit 11 (see FIG. 2) determines that image fogging has occurred in the copy output from the electrophotographic copying machine based on the output of the copy density sensor Os (see FIG. 1), Light intensity sensor X,
The detected values of the surface potential sensor Vs and the toner concentration sensor Ds are stored in the membership function generation unit 13 in FIG.
It is qualitatively quantified based on the membership function shown in.
For example, the detection value of the light amount sensor X is 2.2 as a quantitative value.
Below (V), the parameter X (-: 1.0, N: 0.
It is qualitatively converted to 0). When the detected quantitative value of the light amount sensor X is 2.3 (V), the parameter X (-: 0.7, N:
It is qualitatively converted to 0.3). Further, when the detected quantitative value of the light amount sensor X is 2.5 (V) or more, the parameter X (-:
0.0, N: 1.0).

The detected quantitative value of the surface potential sensor Vs and the detected quantitative value of the toner concentration sensor Ds are similarly qualitatively converted by using the membership function of Vs and the membership function of Ds shown in FIG. .. When it is determined that the image density is low, the detected quantitative values of the light amount sensor X, the surface potential sensor Vs, and the toner density sensor Ds are determined by using the membership functions of X, Vs, and Ds shown in FIG. Each is qualitatively converted.

Next, a method of setting the membership function shown in FIG. 4 or 5 will be described. Generally, in order to convert the quantitative value detected by the sensor into a qualitative value, it is necessary to define a boundary mark (landmark) on the quantity space. However, considering the change in the normal state of the electrophotographic copying machine after restoration and the limit of the measurement accuracy of the sensor, it is not easy to determine the boundary mark as static. If the boundary mark is determined to be static and there is an error in the determination, the qualitative quantification of the sensor value, which is the premise of this control, will not be performed accurately, and it will not be possible in subsequent failure diagnosis and failure repair. ,
There is a greater risk of misdiagnosis and repair.

Therefore, in this embodiment, as described above, the boundary marker is defined for each failure symptom, and the boundary marker is defined by using the membership function of the fuzzy theory. If the detected quantitative value of the sensor is made into a qualitative value by using the membership function according to the failure symptom, the reading error of the sensor,
It is possible to flexibly and suitably cope with a change in sensor output due to a change in the usage environment.

Further, when the detected quantitative value of the sensor is made into a qualitative value, if the membership function of the fuzzy theory is introduced, the correspondence between the actually measured quantitative value and the qualitative value which depends on the measurement accuracy of the sensor and changes in the usage environment. It is possible to flexibly deal with the problem relating to marking, and it is possible to prevent an error from occurring easily when the sensor value is qualitatively converted. At this stage, the qualitativeized parameters are not immediately applied to the set of qualitative values and used to select any failure.
As will be described later, one of a plurality of faults included in the set of qualitative values
In order to select one of them, a failure of a set of qualitative values closest to the state of the parameter is obtained based on a predetermined calculation formula.

Further, in this embodiment, the pseudo fault method (Im
itation Fault method: IF method) was introduced. The IF method forcibly causes a failure in the electrophotographic copying machine by operating the actuator at the initial stage before shipping the electrophotographic copying machine, after repairing the failure, or at any timing based on manual input. This is a method of dynamically determining the boundary mark by using sensor information at the time of normal and before the failure. The membership functions shown in FIGS. 4 and 5 are determined using this IF method. I
If the F method is used, it is possible to dynamically determine the boundary mark on the quantity space necessary for quantifying the detected quantitative value of the sensor for each electrophotographic copying machine that is an actual control target. It is possible to accurately define the boundary mark which forms the basis of each device.

If the IF method is used, as will be described later, the boundary mark defined when the device is in the initial state can be corrected each time the failure repair is completed, so that the device changes with time and the operating environment changes. It is possible to constantly update the boundary marker on the quantity space to an optimum value in accordance with the above. Returning to FIG. 2, the functional block of this electrophotographic copying machine is provided with the pseudo failure generating unit 14 for executing the above-mentioned IF method.

In this embodiment, as shown in FIGS. 4 and 5, the boundary mark on the quantity space is defined by using the membership function of the fuzzy theory. That is, the boundary mark is fuzzy. If you make the boundary mark fuzzy,
As described above, there is an advantage that even if the reading error of the sensor occurs or the boundary mark changes due to disturbance such as environmental change, it can be flexibly dealt with. However, the present invention is not only directed to configurations that use fuzzy theory when defining boundary markers. For example, in defining the boundary mark on the quantity space required when converting the sensor value to the qualitative value described in Japanese Patent Application No. 2-252191 related to the applicant of the present application, the boundary mark is determined by the above-mentioned IF method. The configuration to decide is also
This invention is the subject. This is because the IF method can determine the boundary marker dynamically by itself, that is, without fuzzyizing the boundary marker, and the optimum boundary marker can be obtained.

FIG. 6 shows the diagnosis / repair reasoning unit 11 shown in FIG.
Fuzzy Qualitativ
e Reasoning: FQR) is a flowchart showing an algorithm. Next, the failure diagnosis and failure repair processing in this electrophotographic copying machine will be described along the flow of FIG. When the control operation starts, the diagnosis / repair reasoning unit 1
The detection value of the copy density sensor Os is read by 1 (step S1). Then, the read copy density Os is compared with a predetermined reference value to determine whether or not the electrophotographic copying machine is out of order (step S2).

For example, assume that the conditions shown in FIG. 7 are stored as reference values. That is, the detection voltage is 2.5
If the voltage is less than (V), the image is light, the detection voltage is 2.5 (V) or more and less than 2.9 (V) is normal, and the detection voltage is 2.9.
Above (V), it is assumed that the failure presence / absence determination reference value called image fogging is set. At this time, the copy density sensor O
If the detected value of s is 3.1 (V), it is determined that the failure symptom "image fog" has occurred (step S3).

The processes of steps S1 to S3 are performed because the electrophotographic copying machine according to this embodiment is a machine that automatically determines the presence or absence of a failure. However, this process is performed manually. Good. Step S manually
When performing the processes of 1 to S3, the copy density sensor Os need not be provided. In the manual process, a serviceman or the like may judge that the copy causes image fogging, for example, by looking at the copy output from the electrophotographic copying machine.
In this case, the image fog is input to the apparatus as a failure symptom. The failure symptom may be input by using a numeric keypad or the like which is usually provided in the electrophotographic copying machine.

When the failure symptom "image fog" is determined in step S3, the detection values of the light amount sensor X, the surface potential sensor Vs and the toner density sensor Ds are read (step S4). Now, the detected values of each sensor read are X: 2.45 (V), Vs: 2.5 (V), D
It is assumed that s is 1.9 (V). The read sensor values are applied to the membership function at the time of image fogging (FIG. 4) stored in the membership function generation unit 13,
A tentative qualitative value is determined (step S5). In this specific example, X: 2.45, Vs: 2.5, Ds: 1.9,
Fitted to the membership function of Figure 4, respectively,
X: -0.8, Vs: +0.9, Ds: +0.7 are obtained.

That is, (X, Vs, Ds) = (2.45, 2.5, 1.9) = p (-0.8, +0.9, +0.7) is obtained. If the sensor detection value is qualitatively converted based on a specific boundary marker instead of the fuzzy qualitative quantification using the membership function, (X, Vs, Ds) = (-, +, +) Is obtained.

Next, the failure symptom "image fogging" shown in Table 1
A set of qualitative values of is determined in the output unit 12, and the degree of coincidence C between the failures listed in the set of qualitative values and the tentative qualitative value obtained in step S5 is calculated (step S).
6). The calculation of the degree of coincidence C is performed as follows. First, the failures listed in the set of qualitative values of the failure symptom "image fog" are expressed in a three-dimensional quantity space of X, Vs, and Ds. This expression can be expressed by the following equation.

Hl defect: (X, Vs, Ds) = f1 (-1.0, +1.0, +1.0) Vn defect: (X, Vs, Ds) = (N1.0, +1.0, +1) .0) = f2 (−0.0, + 1.0, + 1.0) Vb defect: (X, Vs, Ds) = (N1.0, N1.0, + 1.0) = f3 (−0.0) , +0.0, +1.0) Vt failure: (X, Vs, Ds) = (N1.0, N1.0, N1.0) = f4 (-0.0, +0.0, +0.0) Above When the equation (3) is plotted, the three-dimensional quantity space shown in FIG. 8 is obtained. In FIG. 8, f1, f2, f3, and f4 are the positions of the Hl defect, the Vn defect, the Vb defect, and the Vt defect, respectively.

The temporary qualitative value p (-0.8, +0.9, +0.7) obtained in step S5 is located at p in the three-dimensional quantity space of FIG. Therefore, next, from the point p, the positions f1, f2, f of each fault listed in the set of qualitative values
The distance D to 3 and f4 is calculated as follows. D (f1) = √ {( 0.8-1.0) 2 + (0.9 -1.0) 2 + (0.7 -1.0) 2} = 0.374 D (f2) = √ {(0.8-0.0) 2 + (0.9 -1.0) 2 + (0.7-1.0) ² } = 0.86 D (f3) = √ {(0.8-0.0) ² + (0.9 -0.0) ² + (0.7 -1.0) ² } = 1.241 D (f4) = √ {(0.8- 0.0) ² + (0.9 -0.0) ² + (0.7 -0.0) ^2} = 1.393 Then, the distance D calculated in the above equation is normalized matching score C is calculated. The normalization of the distance D is performed based on the following formula.

C = 1-D / √n (where n is the number of sense parameters: in this case, n =
3) Therefore, each matching degree C is C (f1) = 1-0.374 / √3 = 0.784 C (f2) = 1-0.86 / √3 = 0.503 C (f3) = 1 −1.241 / √3 = 0.284 C (f4) = 1−1.393 / √3 = 0.196. As a result, f1, which is the closest to the distance D from the point p,
That is, f1 (H1 defect) having the highest degree of coincidence C is determined as a failure candidate (step S7).

The formula for calculating the degree of coincidence C can be expressed by the following general formula. C = 1−√ {C (p1) ² + C (p2) ² + ... + C (pn) ² } / √n C (pn) = Gm (qn) -Gs (qn) (where C: coincidence of the entire model Degree, pn: measurable variable, C (pn): degree of agreement with variable pn, qn: qualitative value that variable pn can take, Gm (qn): grade of qualitative value qn in failure model, Gs (qn): measurement (Grade of qualitative value qn in the value) When performing normal qualitative quantification based on a specific boundary marker instead of fuzzy qualitative quantification, the degree of coincidence C in step S6
Is omitted, and the failure is immediately determined to be the Hl failure from (X, Vs, Ds) = (-, +, +).

In step S7, since the failure is determined to be H1 failure, the repair method (method shown in Table 3) corresponding to the failure "H1 failure" of the failure symptom "image fog" stored in the output unit 12 is determined. , According to their priority. In order to perform the restoration method in order of priority, the counter x is cleared in step S8, and the counter x is cleared in step S9.
Is set to x = 1. Next, when it is confirmed that the value of the counter x does not exceed the registered number of stored repair methods (step S10), the priority of the value of the counter x of the stored repair methods (for example, When the first restoration is performed, the restoration of Hl: UP (increasing the halogen lamp light amount) having the priority No. 1 is performed (step S11).

Then, it is judged whether or not this repair is successful (step S12). Whether or not the restoration is successful is made by performing copying after the restoration and reading the density of the copy output as a result by the copy density sensor Os. If the repair was unsuccessful, step S
Returning to 9, the count value of the counter x is incremented by 1, and the restoration of the next priority is performed. For example, the priority N
o. Vn: DOWN (reducing the main charger voltage), which is the second restoration, is performed. If the restoration of the next priority is not registered, the processing ends at that point.

When it is determined in step S12 that the repair is successful, the process proceeds to step S13, the IF method is executed, and the process ends. FIG. 9 shows the processing content of the IF method performed in step S13 described above. Next, the IF method will be described in detail with reference to FIG. When the failure repair is successful, the diagnostic / repair inference unit 11 reads the detection values of the light amount sensor X, the surface potential sensor Vs, and the toner concentration sensor Ds (step S21). The detection value of each sensor read at this time is, for example, X: 2.9 (V), Vs:
It is assumed that they are 1.6 (V) and Ds: 1.4 (V).

Next, the pseudo fault generating section 14 (see FIG. 2)
Thus, the halogen light amount controller 3C is operated, and the light amount of the halogen lamp 3 is reduced (step S2).
2). Then, each time the light amount of the halogen lamp 3 is reduced by a small amount, the electrophotographic copying machine is caused to perform a copying operation, the copy density obtained at that time is detected by the copy density sensor Os, and the detected value is read (step S23). ).
The detected value of the copy density sensor Os is compared with the reference value for determining the presence / absence of failure in FIG. 7, and when the value of Os reaches the reference value at which image fog occurs, the process of lowering the light amount of the halogen lamp 3 is stopped. (Step S24).

Then, the detection values of the light quantity sensor X, the surface potential sensor Vs and the toner density sensor Ds when the light quantity of the halogen lamp 3 is lowered until the image fog occurs are read (step S25). The read detection values are, for example, X: 2.6 (V), Vs: 2.5 (V), Ds:
It is assumed to be 1.8 (V). The detected values of X, Vs, Ds after the failure read in step S21 and the detected values of X, Vs, Ds at the time of image fog read in step S25 are sent to the membership function generation unit 13. Given, the membership function at the time of image fogging is generated. That is, the value detected in step S21 is used as the boundary marker at the normal time, and the value read in step S25 is used as the boundary marker at the start of image fog generation. The membership function at the time of image fog shown in FIG. It is restored to the membership function shown in.

Next, the pseudo failure generating section 14 operates the halogen light quantity controller 3C to increase the light quantity of the halogen lamp 3 (step S26). Then, each time the light quantity of the halogen lamp 3 is increased by a small amount,
The electrophotographic copying machine is made to copy, the copy density obtained at that time is detected by the copy density sensor Os, and the value is read (step S27).

Then, when the read value of the copy density sensor Os is illuminated with the failure presence / absence determination reference value of FIG. 7 and the image reaches a light copy (step S28), the light quantity sensor X and the surface potential sensor Vs at that time are detected. And the detection value of the toner density sensor Ds is read (step S29). This read value is, for example, X: 3.5 (V), Vs: 0.6.
(V) and Ds: 0.5 (V).

The read value is sent to the membership function generator 13. In the membership function generator 13, the normal sensor value read in step S21,
With the sensor values at the time of generating the low-density image read in step S29 as the boundary marks, the membership function at the time of the density decrease is generated. As a result, the membership function at the time of concentration decrease shown in FIG. 5 is restored to that shown in FIG.

The above-described IF method may be performed in response to the manual input of the IF method execution signal by a service person, for example, other than after the failure repair is successful. The present invention is not limited to the contents of the embodiments described above, and various modifications can be made based on the scope of the claims. For example, although the above-described embodiment has been described by taking a small electrophotographic copying machine as an example, the self-diagnosis and self-repair system according to the present invention is also applied to other image forming apparatuses such as a laser beam printer and a facsimile. be able to.

Further, in the embodiment, in an electrophotographic copying machine, it is assumed that a failure symptom appears when the obtained copy image is not beautifully finished, and an apparatus for self-repairing the failure which is the cause of the development of the failure symptom. I have been explaining all the time. However, the present invention can also be applied to self-diagnosis and self-repair for other malfunctions of the image forming apparatus that differ from whether or not the copy image is beautifully finished.

Other various changes are possible.

[0070]

According to the present invention, when the detection value of the sensor provided in the image forming apparatus is converted into a qualitative value, the conversion is influenced by the usage environment of the apparatus and the detection accuracy of the sensor. It is possible to accurately estimate the failure causing the failure symptom in the image forming apparatus by using the converted fuzzy qualitative value without any variation.

Furthermore, according to the present invention, the membership function as a boundary mark, which is the basis for quantifying the sensor detection value, can be accurately defined for each device. Moreover, since the membership function can be updated at a predetermined timing, the sensor detection value can always be qualitatively and accurately, and a device without erroneous diagnosis or erroneous repair can be obtained.

[Brief description of drawings]

FIG. 1 is a machine configuration diagram of a small electrophotographic copying machine to which the present invention is applied.

FIG. 2 is a functional block diagram of the small electrophotographic copying machine shown in FIG.

FIG. 3 is a simplified parameter model of the small electrophotographic copying machine shown in FIG.

FIG. 4 is a diagram showing membership functions of X, Vs, and Ds used for image fogging.

FIG. 5 is a diagram showing membership functions of X, Vs, and Ds used when image density is reduced.

FIG. 6 is a flowchart showing an algorithm of fuzzy qualitative inference.

FIG. 7 is a diagram showing an example of a failure presence / absence determination reference value.

FIG. 8 is a diagram in which the failures listed in the set of qualitative values of the failure symptom “image fog” are expressed in a three-dimensional quantity space of X, Vs, and Ds.

FIG. 9 is a flowchart showing the processing contents of a pseudo failure method (IF method).

FIG. 10 is a diagram showing a membership function when an image is fogged and corrected by the IF method.

FIG. 11 is a diagram showing a membership function corrected by the IF method when the image density is reduced.

[Explanation of symbols]

 1 Photoconductor drum 2 Main charger 3 Halogen lamp 4 Developing device 5 Transfer / separation charger 2C Main charger controller 3C Halogen light intensity controller 5C Transfer charger controller 11 Diagnostic / repair reasoning unit 12 Output unit 13 Membership function generation unit 14 Pseudo-fault occurrence unit X Light intensity sensor Vs Surface potential sensor Ds Toner density sensor Os Copy density sensor

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Tetsuo Tomiyama 1035 Hanazonocho, Chiba City, Chiba Prefecture 2-203 (2) Inventor ▲ Yoshi ▼ Hiroyuki Kawa 804 Yonbancho, Yonbancho, Chiyoda-ku, Tokyo 804

Claims (7)

[Claims] 1. An image forming apparatus capable of self-diagnosing a failure occurring in the apparatus, comprising: output means for outputting a set of qualitative values of parameters representing a failure state of the apparatus; A plurality of sensors for detecting the state of the body part and the boundary markers of the qualitative quantity space, which are provided for each sensor and are necessary for converting the detected value of each sensor into a qualitative value, are the membership functions of the fuzzy theory. Boundary mark storage means stored as, and the detection values of the plurality of sensors that are read before the failure is forcibly caused to cause a failure in the device and the plurality of sensors that are read when the failure is caused. Boundary detection means for correcting the membership function stored in the boundary storage means using the detected value of the sensor of the sensor, and when a failure symptom appears in the device, the plurality of cells are detected. A detection value of the sensor, and using the membership function stored in the boundary mark storage means, a conversion means for converting the detection value of each sensor into a fuzzy qualitative value; and a fuzzy qualitative value converted by the conversion means. Selector means for comparing a set of qualitative values output from the output means and selecting a set of qualitative values having a predetermined relationship with the fuzzy qualitative value in order to identify a fault causing a failure symptom under development. An image forming apparatus capable of self-diagnosis, including: 2. The image forming apparatus according to claim 1, further comprising a failure repairing unit that operates to repair a failure set in the set of qualitative values selected by the selecting unit. To do. 3. The image forming apparatus according to claim 2, wherein the boundary mark correcting means corrects the membership function, so that the timing for forcibly causing a failure in the apparatus is:
It is characterized in that it is the number of times the failure repair is completed by the failure repair means. 4. The image forming apparatus according to claim 2, wherein the boundary mark correcting means corrects the membership function so that the timing of forcibly causing a failure in the apparatus is:
It is characterized in that the correction request signal is input by manual operation or the like. 5. The image forming apparatus according to claim 1, wherein the membership functions stored in the boundary mark storage means are stored for each failure symptom. 6. The image forming apparatus according to claim 1, 2, 3 or 4, wherein the selection unit defines a qualitative amount space, and a set of qualitative values,
The fuzzy qualitative value is compared with the positional relationship in the qualitative amount space, and a set of qualitative values closest to the position of the fuzzy qualitative value is selected. 7. The output means according to claim 1, wherein the image forming apparatus is qualitatively expressed by a causal relationship network of parameters representing the characteristics of the apparatus, and it is assumed that a failure symptom appears in the apparatus. All the possible states of the device are qualitatively simulated using a causal relationship network of the parameters, and the results are output.