JPH04273263A - Self-diagnostic system for image forming device - Google Patents

Abstract

PURPOSE:To quickly obtain a deterioration quantity, and to more accurately specify the cause of a fault by considering/specifying the degree of the occurrence of a fault phenomenon while treating a fuzzy value that the deterioration quantity is considered. CONSTITUTION:The deterioration quantity from a storage part 15 is read based on a using time up to now and turned into a fuzzy quality value. A parameter value changed by the deterioration quantity is displayed with the fuzzy quality value. Then, the degree of the occurrence of the fault phenomenon by the deterioration value is calculated. On the other hand, inputted state data is turned into the fuzzy quality value, and the presence of the fault is decided with the fuzzy quality value. Consequently, when the fault is present, the diagnosis of the fault is carried out by using the parameter value changed by the deterioration value as an initial value the cause of the fault is identified based on the comparison of the state data turned into the fuzzy quality value and the diagnostic result and the calculated degree of the occurrence of the fault phenomenon.

Description

[Detailed description of the invention]

0001

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a self-diagnosis and/or self-repair system. More specifically, in addition to utilizing artificial intelligence and knowledge engineering, which have been actively researched in recent years, we also employ fuzzy reasoning to self-diagnose the deterioration status and operating status of equipment, and self-repair as necessary. It is about a system that can

0002

[Prior Art] In the field of development of precision machinery and industrial machinery, artificial intelligence (AI) has recently been introduced in order to save labor in maintenance work and extend automatic operation.
Research into expert systems using so-called AI technology is actively being conducted. Some expert systems can self-diagnose whether a failure has occurred in a device and self-repair the failure that has occurred.

[0003] However, in conventional fault diagnosis systems using expert systems, (A) the knowledge is not versatile and fault diagnosis cannot be performed for various targets; (B)
) The inability to diagnose unknown faults; (C) As the target becomes more complex, the amount of knowledge required for fault diagnosis increases explosively, making it difficult to implement; (D) Difficult to acquire knowledge. , etc. limitations were pointed out.

More specifically, conventional automatic adjustment systems and fault diagnosis systems basically operate corresponding actuators based on the output of a certain sensor. In other words, a type of automatic adjustment and failure diagnosis was performed using a predetermined combination of sensors and actuators. Therefore, basically, a certain sensor corresponds to a specific actuator, and the relationship between the two is fixed. Therefore, (a) the relationship between sensor parameters and actuator parameters must be numerically specified. (b) For the reason (a) above, the relationship between the sensor parameters and the actuator parameters is strongly dependent on the object, and therefore lacks versatility and cannot be used for various objects. (c) The relationship between the parameters of each sensor or the parameters of each actuator is unrelated to control, and only simple control based on the relationship between the corresponding sensor parameters and actuator parameters can be performed, and failures that can be dealt with is limited in advance, and unknown failures cannot be handled. (d) Due to the reason in (c) above, there are problems such as the inability to predict the side effects on the parameters of other actuators that may occur due to the manipulation of the parameters of any actuator.

As described above, in conventional automatic adjustment systems and fault diagnosis systems, predictive failure A is performed based on set A of sensor A and actuator A, and predictive failure B is performed based on set B of sensor B and actuator B. The predicted failure C is a set of sensor C and actuator C.
Fault diagnosis was performed based on each independent sensor and actuator set, and fault repair was performed based on the diagnosis.

[0006]

[Problems to be Solved by the Invention] Therefore, as a technology related to the present invention, the applicants of the present application first took up an image forming apparatus as a target machine, and developed a new self-diagnosis and /or proposed a system that performs self-repair (Japanese Patent Application No. 2-252111 or 2003)
-252216).

The qualitative reasoning used in the self-diagnosis and/or self-repair systems proposed above is complete as a method for determining qualitative transitions from a group of equations and an initial state. On the other hand, since reasoning is done qualitatively, in other words, in a symbolic form of expression, there is an unavoidable fate that does not allow ambiguous expressions as a state expression of the target system (machine). There is. This is insufficient as a method for diagnosing and repairing failures by handling "ambiguous information" that is often encountered in maintenance activities, such as information such as "this may be normal or may be abnormal" regarding the machine's status. It is.

[0008] Furthermore, when considering integrating failure diagnosis using deterioration and failure history information of individual parts constituting a machine, it is necessary to use some other expression method for the qualitative inference proposed previously. Unless we consider an inference method that adds a method to handle ambiguous information by adding together the logic that has been used in the past, it is not possible to construct a more complete fault diagnosis and/or fault repair system.

[0009] Therefore, the inventor of the present application has combined fuzzy theory, which is a theory that deals with ambiguity mathematically, with the qualitative reasoning used in the above-mentioned self-diagnosis and/or self-repair systems. Invented advanced self-diagnosis and/or self-healing systems. The specific purpose of this invention is to develop reasoning that allows ambiguity from a maintenance standpoint, and to use this reasoning to self-diagnose the state of a machine, specifically an image forming apparatus. The objective is to provide a system that is capable of self-repairing when necessary.

0010

[Means for Solving the Problems] A self-diagnosis system for an image forming device that embodies image data to generate a visible image, in which the image forming device is expressed as a combination of a plurality of elements, and each a target model storage means in which qualitative data qualitatively expressing the behavior or attributes of the elements and the coupling relationship between each element using parameters, membership functions of each parameter, failure phenomenon occurrence reference values, and failure diagnosis knowledge are stored; Deterioration data storage means that stores deterioration data representing the relationship between usage time and amount of deterioration for predetermined parts constituting the image forming apparatus, data input means into which arbitrary data can be input, and deterioration data storage means. Deterioration amount calculation means calculates the amount of deterioration for the usage time up to now based on the deterioration data stored in The value of the parameter that changes depending on the amount is expressed as a fuzzy qualitative value using the membership function of the parameter, and the degree of occurrence of a failure phenomenon that appears in the image forming apparatus according to the amount of deterioration is calculated using the failure phenomenon occurrence reference value. change calculation means for converting the data, data conversion means for converting data given from the data input means into fuzzy qualitative values using membership functions of parameters stored in the target model storage means, and target model storage means for converting the conversion output of the data conversion means. a failure determination means for determining the presence or absence of a failure by comparing it with qualitative data stored in the target model storage means, in response to the failure determination means determining the presence of a failure, using the calculation result of the change calculation means as an initial condition; A fault diagnosis means that diagnoses the state of the image forming apparatus based on the qualitative data and fault diagnosis knowledge stored in the image forming apparatus, and outputs the diagnosis result in an expression containing ambiguity, and fuzzy qualitative values and faults converted by the data conversion means. The present invention is characterized by comprising cause identification means for identifying the cause based on a comparison with the state of the image forming apparatus diagnosed by the diagnostic means and the degree of occurrence of a failure phenomenon appearing in the apparatus calculated by the change calculation means. .

[0011]

[Operation] The self-diagnosis system according to the present invention has the following features:
The amount of deterioration with respect to the usage time up to the present is calculated based on the deterioration data representing the relationship between the usage time and the amount of deterioration. Then, the value of the parameter that has changed according to the calculated amount of deterioration is converted into a fuzzy qualitative value using the membership function of the parameter, and is replaced with an expression used on the qualitative data, and the degree of occurrence of a failure phenomenon caused by the amount of deterioration is calculated. Further, the data input from the data input means is converted into a fuzzy qualitative value using a parameter membership function, and the presence or absence of a failure is determined based on the fuzzy qualitative value. If it is determined that there is a failure, failure diagnosis is performed using the parameter values changed according to the amount of deterioration as initial values. Diagnosis results are output with ambiguous expressions.

Furthermore, the cause of the failure is identified by comparing the fuzzy qualitative value obtained by converting the input value of the data input means with the results obtained in the failure diagnosis. In other words, when multiple failure cause candidates are selected as a result of failure diagnosis, which failure cause candidate is more consistent with the actual device condition is determined based on the degree of agreement between parameter values and the degree of occurrence of failure phenomena. Consider based on. Therefore, the actual fault condition is more accurately identified and output.

[0013]

[Example] Fuzzy Qualitative Reasoning First, fuzzy qualitative reasoning, which is newly developed reasoning with ambiguity necessary for self-diagnosis, will be explained. (1) Fuzzy qualitative value In the qualitative reasoning used in the self-diagnosis and/or self-repair system related to the applicant's earlier application, the concept of quantitative space and qualitative value is used as a method for symbolically expressing the value of a variable. is used. A quantity space is a finite set in which a set of real numbers is symbolically expressed by landmarks, which are physically meaningful characteristic values, and intervals surrounded by these landmarks. Therefore, a qualitative value can only take either a boundary value or an interval value.

When considering a self-diagnosis and/or self-repair system using an ideal target machine as an example, it would be meaningful to determine whether a certain value is a boundary value or an interval value. However, in the real world, when we consider converting quantitative values obtained through measurement into qualitative expressions and making inferences based on them, we simply choose between boundary values and interval values. There is a risk that it may lack validity. This is because, as mentioned above, "ambiguous information" can be found in conservation activities, and situations may actually occur where the information may be a boundary value or an interval value.

[0015] Therefore, in this invention, we decided to consider the application of fuzzy theory. Fuzzy theory is a theory that deals with ambiguity mathematically, and a feature of the representation of sets in fuzzy theory is that the intermediate state of whether or not an element belongs to a set is
0.0 to 1.0 as the degree to which the element belongs to the set
It is expressed as a decimal value up to . By using this representation format, it is possible to represent intermediate states that cannot be represented using conventional sets. In fuzzy theory, a function that defines the degree (grade) that a certain element belongs to a certain set is called a "membership function."

By introducing the concept of a fuzzy set expressed using this membership function, it becomes possible to express qualitatively with added ambiguity. That is, in this invention, the value of a variable is expressed as a set of a conventional qualitative value and a degree (grade) belonging to that qualitative value. This type of expression will be referred to as a "fuzzy qualitative value." For example, if the value of a certain variable in the quantity space shown in Figure 1 is expressed as a fuzzy qualitative value, for example (Normal: 0.4, (Normal, nil
):0.6).

By using this method of expressing fuzzy qualitative values, it becomes possible to tolerate ambiguity when using sensor information for inference. In other words, in the previously proposed fault diagnosis system using qualitative inference, a certain range is determined as the normal value range, and if the quantitative value obtained from the sensor is within that range, that value is called the "normal value." Qualitative inference was made based on the fact that it was on the landmark of ``.

In contrast, in the present invention, a membership function is used for converting quantitative values obtained from sensors into qualitative expressions. When using membership functions, as shown in FIG. 2, "Normal values (hereinafter abbreviated as N)",
Larger than normal value (Normal, nil; hereinafter, N,
(abbreviated as nil)", "Less than normal value (0, Norm
A membership function such as "al; hereinafter abbreviated as 0, N" is determined in advance. Then, by mapping the measured values obtained from the sensor onto this space shown in FIG. 2, the measured quantitative value of the sensor is converted into a qualitative expression that allows ambiguity. (2) Operation rules for fuzzy qualitative values The algebraic operations for fuzzy qualitative values consist of algebraic operation rules for qualitative reasoning and grade calculations. This will be explained using a specific example. For example, Zf = Xf × Yf Xf = (N: 0.8, (N, nil): 0.2
) Yf = ((0, N) 0.7, N: 0.3), and the relationship between landmarks is explained using (Xf, Yf, Zf) = (N, N, N) as a specific example. do. First, all possible qualitative values of Zf are listed based on the relationship between the respective qualitative values of Xf and Yf (in this example, N and (N, nil) for Xf, etc.) and landmarks. At that time, the grade for the qualitative value of Zf is the smaller of the grades for the qualitative values of Xf and Yf. Specifically, Xf
Yf Zf N
×(0,N)=(0,N)
: 0.8, 0.7=0.7
N × N = N
: 0.8, 0
．． 3=0.3 (N, nil) × (0, N)=
(0,N)N(N,nil): 0.2, 0.7=
0.2 (N, nil) × N
=(N, nil): 0.
2, 0.3=0.2 Furthermore, when two or more grades for the qualitative value of Zf are determined, the maximum value among them is adopted. In the above example, in the first and third equations, Z
For the qualitative value of f (0, N), 0.7 and 0, respectively.
．． 2 can be found, the maximum value of which is 0.7 as Z
The grade is the qualitative value of f (0, n). Similarly, Zf
Also for the qualitative value N, in the second and third equations, 0.
There are two options, 3 and 0.2, so choose the maximum value of 0.3.

From the above calculation, the fuzzy qualitative value of Zf is Zf=((0,N):0.7,N:0.3,(
N, nil): 0.2). Furthermore, standardization is performed so that when the grades are added together, the sum becomes 1. For standardization, the grade of each qualitative value is set to 1.2 (however,
This is done by dividing by 1.2=0.7+0.3+0.2). As a result of normalization, Zf = ((0, N): 0.58, N: 0.25, (
N, nil): 0.17). (3) Inferential reasoning basically uses the propagation method. This is an algorithm that starts with a parameter whose value has already been determined and propagates the value of that parameter sequentially to other parameters using the relationships between the parameters to determine the parameters of the entire system.

[0020] The propagation method uses a method in which an undetermined parameter in a ternary or binary relationship is determined by the above-mentioned fuzzy operation rule using already determined parameters and the relationship therebetween. The specific method of inference will become clearer in the specific examples described below. System Configuration FIG. 3 is a block diagram showing the configuration of an embodiment of the present invention. This system includes a plurality of sensors 1a installed on the target machine (specifically, a small electrophotographic copying machine, etc.).
, 1b, 1c and a plurality of actuators 6a, 6b, 6c for changing the operating state of the target machine.

The plurality of sensors 1a, 1b, and 1c are each for detecting changes in the elements of the target machine or the related states between the machine elements caused by the operation of the target machine. The information taken in from the plurality of sensors 1a, 1b, 1c is amplified by the amplifier circuit 2, and then sent to the A/D
The analog signal is converted into a digital signal by the conversion circuit 3, and is provided to the digital signal/FQ value (fuzzy qualitative value) conversion section 11. Digital signal/FQ value converter 11
is a part that converts the digital signal given from the A/D conversion circuit 3 into a fuzzy qualitative value. That is, the digital signal is divided into qualitative values (for example, none (0), small (0, N), normal (N), large (N, nil),
any of the four symbols) and grade (0
．． It has a conversion function that expresses it by a numerical value from 0 to 1.0). By converting the signals given from the sensors 1a, 1b, and 1c into qualitative information represented by fuzzy qualitative values, functional evaluation can be performed more accurately, as will be described later.

The deterioration data calculation section 12 also includes a timer 19 for providing usage time data to the deterioration data calculation section 12.
, a short-term simulation section 13, a fault diagnosis section 14, a target model storage section 15, and a fault simulation section 16. The deterioration data calculation unit 12
This is the part for calculating the amount of deterioration over time of the parts that make up the target machine. The details of the calculation method will be described later. The short-term simulation section 13 is a section for simulating the current state of the target machine. The fault diagnosis section 14 is a section for performing functional evaluation using the fuzzy qualitative values provided from the digital signal/FQ value conversion section 11, identifying fault symptoms, and deriving the cause of the fault from the fault symptoms. A processing step (
The fault diagnosis step) is performed based on the inference (non-fuzzy inference that does not use fuzzy inference) used in the self-diagnosis and/or self-repair system disclosed in the above-mentioned specification of the applicant of the present application.

[0023] The target model storage unit 15 stores a "substantive model" (see Table 1 to be explained later) in which the target machine is viewed from a physical perspective and is represented by parameters as a combination of a plurality of elements at the substantive level, and each parameter. "Parameter model" expressed as a join tree (see Figure 11, explained later)
, the initial value of each parameter, the membership function of the functional parameter (as shown in Figure 2), the relationship between usage time and deterioration state of the part of interest (see Figure 4, which will be explained later),
The relationship between the degradation state and the parameter membership function (
(See FIG. 5, which will be explained later), the relationship between the deterioration state and the degree of occurrence of a failure phenomenon (see FIG. 6, which will be explained later), failure determination reference values, failure candidate knowledge, etc. are stored in advance. This knowledge stored in the target model storage section 15 is utilized when the digital signal/FQ value conversion section 11, the deterioration data calculation section 12, the short-term simulation section 13, or the failure diagnosis section 14 performs processing. Furthermore, the failure simulation unit 16 is a unit that performs failure simulation in collaboration with the short-term simulation unit 13 and the failure diagnosis unit 14.

One of the features of this embodiment and the present invention is that the system is equipped with two components: a deterioration data calculation section 12 and a short-term simulation section 13. Next, these two components will be explained in more detail. (1) Deterioration Data Calculation Unit The deterioration data calculation unit 12 picks up the parts that are relevant to failure diagnosis among the parts that make up the target machine, and determines the deterioration state of each part over time and the effect that the deterioration state has on parameter values. and calculate the degree to which the failure phenomenon occurs.

For parts related to failure diagnosis, deterioration data representing the relationship between usage time and amount of deterioration is set in advance for each part or for each group of related parts. For example, data as shown in FIG. 4 is set. This deterioration data is stored in the target model storage section 15. The deterioration data calculation unit 12 calculates the deterioration state of the component of interest by integrating the time data given from the timer 19 to obtain the total usage time of the machine up to the present time, and applying that time to FIG. 4. .

[0026] Note that the deterioration state calculated in this case is:
It is a quantity such as the susceptibility to failure, and does not predict the physical quantity on the parameter model of the target machine. For example, even if the part of interest is a specific gear and the state of deterioration of that gear (the amount of gear wear) is determined, it is not known what changes will occur in the parameter model of the target machine as a result. Therefore, next, the effect of deterioration on the target machine is determined. This is shown in Figure 5.
It is determined from the relationship between the state of deterioration and the parameter values of the parameter model as shown in . The relationship between the deterioration state and the parameter value shown in FIG. 5 is set in advance using a membership function of the parameter, and is stored in the target model storage unit 15. In the deterioration data calculation unit 12, the target model storage unit 1
By reading out the relationship graph shown in FIG. 5 from 5 and mapping the deterioration state on this graph, the influence of deterioration on the parameter value is calculated.

Further, the deterioration data calculation unit 12 calculates the relationship between the deterioration state and the degree of occurrence of the failure phenomenon, as shown in FIG. 6, which is stored in advance in the target model storage unit 15.
The degree of occurrence of a failure phenomenon with respect to the calculated deterioration state is determined. Note that the deterioration state calculated based on FIG. 4 is a value that is uniquely determined once the usage time is determined, and is calculated as a quantitative value. On the other hand, the parameter values obtained based on FIG.
, N): 0.9, N: 0.1). Furthermore, the degree of occurrence of the phenomenon calculated based on FIG. 6 is also a quantitative value that is uniquely determined according to the state of deterioration.

In this example, changes in parameter values and the degree of occurrence of a failure phenomenon were determined based on a graph of the state of deterioration versus usage time and a graph of the state of deterioration and the degree of occurrence of a failure phenomenon. The amount itself may be determined, and the parameter value or the degree to which a failure phenomenon occurs may be determined based on a graph representing the amount of deterioration and a change in the parameter value or the degree to which a failure phenomenon occurs. (2) Short-term simulation section Short-term simulation (hereinafter referred to as SSIM) is a simulation for determining the current state of the target machine.

The state of a machine is represented by a set of physical quantities representing the attributes of the individual parts constituting the target machine. SSIM is performed on a parametric model (see FIG. 11) in which these physical quantities are related by qualitative equations. The inference method of SSIM uses the above-mentioned fuzzy qualitative inference. In addition, as an algorithm for fuzzy qualitative inference,
Use propagation method. The method of propagation using this propagation method will be explained below.

The inference is based on constant parameters, parameters determined by values obtained from the sensor, and deterioration data calculation unit 1.
The process begins with the values of the parameters determined by 2 being determined. When propagating, first, (1)
Among the parameters in a ternary relationship (+, -, ×, etc.),
If two terms have already been determined, decide the remaining term.

(2) If one of the parameters in the binary relationship (=) is determined, the other is determined. The above propagation method is repeated until the values of all parameters are determined. As a result, the SSIM determines the state of the entire target machine, in other words, all parameter values.

Next, a procedure for inferring the diagnosis results performed in the deterioration data calculating section 12, the short-term simulation section 13, the fault diagnosis section 14, and the fault simulation section 16 will be explained. Fault diagnosis by fuzzy qualitative inference using deterioration information Referring to FIGS. 7, 8, and 9, inference for fault diagnosis is performed in the following steps.

[0033] In advance, the current state of the target machine is sensed by a sensor installed in the target machine (step S1), and the values of each parameter obtained by the sensing are converted into fuzzy qualitative values (step S2). Sensing of the parameter value in step S1 is of course performed by a sensor, if a sensor is provided (for example, as described later, the light amount Hl of a halogen lamp is measured by an AE sensor, etc.). )but,
If a sensor is not provided, a serviceman or the like may manually measure the target machine and input the measured values into the system. Also, step S2
The fuzzy qualitative value conversion in is performed by mapping the measured quantitative value onto the membership function (as shown in FIG. 2) of each parameter stored in advance in the target model storage unit 15.

(1) Calculation of deterioration state Next, the deterioration state up to the present time is calculated (step S3). This deterioration state is caused by the timer 19 as described above.
The deterioration data calculation unit 12 integrates the time data given from the target model storage unit 15 and applies the integrated value to the graph shown in FIG. 4 read out from the target model storage unit 15. In addition, assuming that the timer 19 itself is of a time accumulation type,
The output value may be used as is, or
Alternatively, the timer 19 may not be provided and the usage time may be manually input by a service person or the like.

(2) Converting the state of deterioration into the influence on the target model Here, the state of deterioration obtained from the graph shown in FIG. 4 is converted into the influence on the parameters of the target model (step S4). The conversion consists of (i) applying the deterioration state calculated in step S3 to the graph in FIG. 5 to obtain changes in parameter values on the parameter model, and applying the deterioration state to the graph in FIG. 6 (ii)
) processing to obtain changes in the degree to which the phenomenon occurs.

In this explanation, the above two changes are determined as effects of deterioration on the target model, but for example, only changes in parameter values on the parameter model may be determined. (3) SSIM Among the influences on the parts determined in (2) above, a simulation is performed using the parameter values obtained as a result of the influence on the parameter values as the initial condition, and the values of the parameters of the entire target machine after the elapse of time are determined. do.

Specifically, the values of the predetermined parameters obtained in step S4 are placed on the parameter model (
In step S5), the values of all parameters of the target machine are determined by propagation on the parameter model using the propagation method, and a current state model of the target machine is generated (step S6). (4) Failure determination Next, among the sensor values that have been converted into fuzzy qualitative values in advance in step S2, the sensor values of the functional parameters are checked to determine whether or not there is a failure (step S7).

The sensor values of the functional parameters are evaluated by comparing them with failure determination reference values stored in the target model storage section 15 in advance. When the sensor value of the functional parameter is determined to be normal, the process proceeds to step S8, and the parameter value on the parameter model obtained in step S6 and the actual parameter value sensed or inputted in step S1 are subjected to fuzzy qualitative analysis in step S2. The converted values are compared to determine the degree of agreement between the parameter model and the actual state of the target machine.

As a result of the matching degree evaluation in step S8, if they match, the process ends. Further, even if they do not match, the machine continues to operate, giving priority to the function parameter determined to be normal in step S7. However, since the model and sensor values do not match and there is still a possibility of failure, a message is displayed on a display device, etc. (step S9).
). There are various possible message display formats, but for example, as mentioned above, if the functional parameters are normal but the models do not match, it is possible that the sensor that measures the functional parameters is malfunctioning. ``This may be the case.''

(5) Failure Diagnosis If a failure is determined in the above (4), failure candidates are derived from the failure symptoms (step S10). A plurality of failure candidates are stored in advance in the target model storage unit 15 (FIG. 3). Tracing is performed on the parameter model from the parameter values of the functional parameters, and a corresponding fault candidate is selected and derived from a plurality of fault candidates stored in advance. Alternatively, the reasoning explained in the applicant's earlier application mentioned above (non-fuzzy reasoning that does not use fuzzy theory)
Failure candidates may be determined by

(6) SSIM For each fault candidate derived in step S10, SS
Perform IM and create a failure model. More specifically,
For each failure candidate, the failure conditions and the parameter values obtained as a result of calculating the deterioration state and converting the effects on the model are placed on the parametric model as initial conditions (step S11), and the parametric model is created using the propagation method. A state model of the target machine is generated by tracing (step S12).

[0042] In this way, a failure model is created. (7) Identification of failure causes Rank and narrow down the causes of failure based on the sensor information and the degree of occurrence of the phenomenon determined in (ii) of (2). More specifically, the degree of matching of the model is evaluated based on the parameter values of the state model and the value obtained by converting the sensing value obtained in step S2 into a fuzzy qualitative value (step S13), and the evaluation result of the model is The degree of occurrence of the phenomenon is re-evaluated and prioritized (step S14).

[0043] By omitting the process of step S14,
A simple method may be used in which the cause of the failure is identified only based on the degree of coincidence of the parameter values performed in step S13. Fault diagnosis is completed by the above reasoning procedure. Furthermore, upon completion of the failure diagnosis, work may be performed to add and/or modify the failure history information. and,
Thereafter, it is determined whether the failure can be repaired by parameter manipulation (step S15), and if it is repairable by parameter manipulation, the repair is performed (step S16).
If it cannot be repaired by parameter manipulation, for example, if a halogen lamp burns out in an electrophotographic copying machine, the process ends as it is because it cannot be repaired. The repair operation in step S16 is performed in the repair planning section 17, which will be described below.

Returning to FIG. 3, the remaining structural blocks will be explained. When there is a failure, the repair planning unit 17
deducing a repair plan for repairing the failure;
This is a component for deriving a repair plan. The inference of the repair plan and the derivation of the repair work are based on the previously proposed self-diagnosis and/or
Alternatively, similar to the reasoning in self-healing systems, non-fuzzy qualitative reasoning that does not use fuzzy theory is utilized.

The repair work output from the repair planning section 17 is converted into a digital signal by the symbol/digital signal conversion section 18. The converted digital signal is converted from a digital signal to an analog signal by a D/A conversion circuit 4, amplified by an amplifier circuit 5, and given to a plurality of actuators 6a, 6b, 6c. 6c is selectively activated to perform repair work. Explanation using a specific target machine as an example Configuration and status of a specific target machine Next, this system will be applied to an image forming apparatus, more specifically a small electrophotographic copying machine, as a specific target machine. This will be explained using an example.

FIG. 10 is an illustrative diagram of a small electrophotographic copying machine as a specific target machine. In Figure 10, 2
1 is a photosensitive drum, 22 is a main charger, 23 is a halogen lamp for illuminating the original, 24 is a developing device, and 25 is a transfer charger. This specific target machine is provided with, for example, three sensors 1a, 1b, and 1c. That is, the sensor 1a is an AE sensor for measuring the amount of light incident on the photoreceptor drum 21, the sensor 1b is a surface potential sensor for measuring the surface potential of the photoreceptor drum 21, and the sensor 1c is for measuring the density of the image copied on the paper. This is a densitometer for measuring .

Although not shown in FIG. 10, three types of actuators are provided. That is, a main charging volume VR1 for changing the main charging voltage of the photoreceptor drum 21, a lamp volume AVR for controlling the light amount of the halogen lamp 23, and a transfer voltage between the photoreceptor drum 21 and copy paper. Three volumes called a transfer volume VR2 are provided as actuators.

By the way, considering the electrophotographic copying machine shown in FIG. 10 from a physical perspective, the electrophotographic copying machine is expressed as a combination of a plurality of elements at a physical level, and the behavior and attributes of each element and the relationships between each element are explained. When the coupling relationship is qualitatively expressed using parameters, it is as shown in Table 1. The expression format shown in Table 1 will be referred to as a "substantive model." In addition, the representation in Figure 11, which abstracts the entity model and represents it as a connection tree of each parameter, is called a "parameter model".
I will call it.

The "substantive model" and the "parameter model" will be collectively referred to as the "object model." The "target model" is qualitative data that is generally common to image forming apparatuses and is also utilized for failure repair, which will be described later. The contents of this entity model and parameter model are as follows:
It is stored in the target model storage unit 15 (see FIG. 3).

[0050]

[Table 1]

In the physical model shown in Table 1 or the parametric model shown in FIG.
sp, sensor parameters obtained by the sensor X, Vs
, Os, and parameters that may deteriorate, the relationship between usage time and deterioration state is set in advance and stored in the target model storage unit 15. For example, in the case of the parameter Hl, this relationship is as shown in FIG. According to the relationship shown in FIG. 12, usage time data, for example, 1st month, 2nd month, etc.
When ・ is given, the deterioration state of Hl is uniquely D1, D
2,... are determined.

[0052] Each parameter is shown in Fig. 13.
It has a qualitative fuzzy quantity space as shown in . This qualitative fuzzy quantity space is created according to membership functions determined for each parameter, and is stored in the target model storage section 15. Using the qualitative fuzzy quantity space shown in FIG. 13, it is possible to obtain changes in parameter values corresponding to the state of deterioration of the parameter H1. For example, when the deterioration state is D1, the parameter value is (N: 1.0), and when the deterioration state is Dn, the parameter value is ((0,
N): 0.085, N: 0.915).

Furthermore, for each parameter, the relationship between the deterioration state and the degree of failure phenomenon is set in advance and stored in the target model storage section 15. This relationship is shown in FIG. 14, taking the parameter Hl as an example.
The relationship is as shown below. According to FIG. 14, when the deterioration state is D1, the degree of failure phenomenon is 0.9,
When the deterioration state is Dn, the degree of failure phenomenon is 1.
24 can be obtained.

Next, several examples will be explained based on the above explanation. Example 1: Example of normal condition after 1 month (T1) since new product The values obtained by the sensors 1a, 1b, and 1c are converted into fuzzy qualitative values in advance. Specifically, the amount of light measured using the sensor 1a is used as the value of the parameter X. Further, the sensor 1b measures the surface potential Vs after exposure, and the sensor 1c measures the toner density Os on the output paper. Then, when each measured parameter value is mapped onto a fuzzy qualitative quantity space set for each parameter as shown in FIG. 13, the following sensor parameter values are obtained, for example.

X=((0,N):0.1,N:0.9)Vs=(N:
1.0) Os = ((0, N): 0.1, N: 0.9) (1) By integrating the time information given from the prediction timer 19 of the current state of deterioration, the operating time of the machine is calculated. Find T. The current state of deterioration is predicted by applying the usage time T to a graph showing the deterioration state with respect to the usage time for each parameter as shown in FIG. As a result, parameter values as predicted values are determined. Since it is now the first month (T=T1), Hl
The parameter value of is D1.

(2) Examining the influence of deterioration By applying the parameter values of Hl determined in (1) above to FIG. 13, Hl=(N:1.0) is obtained. Further, by applying the above parameter value of Hl to FIG. 14, data of 0.9 times the occurrence rate of a failure phenomenon, such as a phenomenon of halogen lamp disconnection (HlCut), is obtained. That is, p(HlC
ut)=0.9 is obtained.

(3) Using SSIM, infer the current state of the entire machine using the results of (2).
l = (N: 1.0)) and the initial condition (in this case, the deterioration state is not calculated for parameters other than Hl, so all remaining parameters are set to (N: 1.0)). Perform SSIM based on SSIM results,
The parameter values are as follows: All parameter values = (N
:1.0).

H1=(N:1.0) γ0=(N:1
．． 0)D=(N:1.0)Vt=
(N: 1.0) β = (N: 1.0) ζ
=(N:1.0)Vn=(N:1.0)
Asp=(N:1.0)Vb=(N:1.0) (4) Failure determination The following knowledge is used as failure determination criteria. This knowledge is stored in advance in the target model storage unit 15 (see FIG. 3).

(a) Machine function evaluation N value≧0.5→normal→compare model agreement>0.5→
Abnormality→Failure diagnosis (b) Comparison of model and actual measured value Matching degree of machine≧0.5→No failure>0.5→Display “Possibility of sensor abnormality” etc. In the case of this example, (a) Regarding the functional evaluation of the machine, it is sufficient to compare the Os value, so the measured value of Os is converted into a fuzzy qualitative value Os = ((0, N): 0.1, N: 0.9
) to compare. In this case, since the N value is 0.9, N≧0.5, and (a) the functional evaluation is normal.

Next, (b) a comparison is made between the model and actual measured values. Each sensor value is compared with the model resulting from the above (3), and the degree of agreement between the two is determined based on the following conditions. Matching degree = max (min (grade of each item)) As a result, the following result is obtained.

Sensor value
Model value Min value X: (0, N
) 0.1
0 N 0.9
1.0 0.9

max0.9 Vs:
N 1.0 1.0
1.0
ma
x1.0 Os: (0, N) 0.1
0 0
N 0.9 1.0
0.9
ma
x0.9 Also, the degree of coincidence of the entire machine is as follows.

[0062] Matching degree of the entire machine = Average matching degree of each sensor = (0.9 + 1.0 + 0.9) / 3 = 0.93 Note that when determining the matching degree of the entire machine, the average matching degree of each sensor Instead of taking the minimum value, the degree of matching may be determined under stricter conditions. If the above failure determination criterion (b) is applied here, the average degree of coincidence exceeds 0.5, so it is determined to be normal. That is, both (a) and (b) are normal and can continue to be used. In the above examples, (a) the functional evaluation of the machine is normal;
(b) If the comparison result between the model and the actual measurement value is abnormal (in FIG. 8, when proceeding from step S8 to step S9), there is a possibility that there is an abnormality in the sensor that measures the parameter related to functional evaluation, that is, Os. I'm thinking about it, so I'll indicate that. Example 2: Same as example 1, in the first month (T1) after the new product,
Example of failure: Assume that the values of sensor parameters X, Vs, and Os are as follows.

X = (0:0,9, (0,N):0.1)Vs=(
N: 0.1, (N, nil): 0.9) Os = (N: 0
．． 1, (N, nil): 0.9) Also, the parameter Hl
Assume that after (1) predicting the current state of deterioration, (2) examining the influence of deterioration, Hl = (N: 1.0) P (HlCut) = 0.9.

Under such conditions, (3) SSIM
All parameter values of the model obtained = (N: 1
．． 0). (4) Failure determination (a) Functional evaluation of the machine From the fuzzy qualitative value of Os, N=0.1 and N<0.
5, indicating a malfunction. Therefore, it is diagnosed as a failure.

(5) Derivation of Fault Candidates The following information is stored in advance in the target model storage unit 15 as fault candidates. HlCut: Hl = 0 HlOut: Hl = (0, N) VtOut: Vt = (0, N) PaperOut: ζ = (0, N) VbOut: Vb = (N, nil) TonerOut: γ0 = (0, N) MCOut:V
n=(0,N) Note that Out means defective.

[0066] Therefore, the functional abnormality in which Os obtained in (4) above is large, that is, Os = (N, nil), is shown in Figure 1.
1 is traced on the parameter model shown in FIG. 1, and FIG. 15 is obtained. In FIG. 15, parameters with upward arrows have values that may have changed to (N, nil), and parameters with downward arrows have values that may have changed to (0, Nil).
) or (0), and parameters with no arrows have their values unchanged. As a result, among the above failure candidates, HlCut
and HlOut are taken out as failure candidates.

(6) Execute SSIM on the two fault candidates obtained in (5) above, and infer the fault state at that time. That is, SSIM is performed on HlOut to obtain the following two models. Hl = ((0, N): 1.0) D
= (N: 1.0) X = (0: 1.0)
or ((0,N):1.0) β
=(N:1.0) Vs =((N, nil):1.0) Yn
= (N: 1.0) Vb = (N: 1.0) γ0 = (N: 1.0) Ds = ((N, nil): 1.0) Vt
= (N: 1.0) ζ = (N: 1.0) Os = ((N, nil): 1.0) Asp
= (N: 1.0) Sp = ((N, nil): 1.0) Two types are because two values of X occur. This means that the value of X is Hl minus D, but H
Since l = (0, N) and D = (N), if we subtract the normal from those smaller than the normal, the result of the fuzzy operation is either the one smaller than the normal remains, or 0
This is because there are two possible cases:

Furthermore, when SSIM is applied to HlCut, the following two models are obtained. Hl = (0:1.0) D = (N:1.0) X = (0:1.0)
or ((0,N):1.0) β
=(N:1.0) Vs =((N, nil):1.0) Yn
= (N: 1.0) Vb = (N: 1.0) γ0 = (N: 1.0) Ds = ((N, nil): 1.0) Vt
= (N: 1.0) ζ = (N: 1.0) Os = ((N, nil): 1.0) Asp
= (N: 1.0) Sp = ((N, nil): 1.0) (7) Identification of failure cause The degree of agreement between the model and the sensor and the degree of occurrence of the phenomenon.
From this point on, rank and narrow down the causes of failure.

(i) Degree of agreement between model and sensor A. HlOut X=0
B. HlOut X=(0,N)
X: 0.9
X: 0.1
Vs: 0.9
Vs: 0.9Os
: 0.9
Os: 0.9 overall
0.9 Overall 0.63 C. HlCut X=0
D. HlCut X=(0,N)
X: 0.9
X: 0.1
Vs: 0.9
Vs: 0.9Os
: 0.9
Os: 0.9 overall
0.9 Overall 0.63 The degree of agreement between the sensor and the model for each failure candidate obtained as a result of performing SSIM in (6) is determined as described above.

The model obtained in (6) has a failure cause (for example, HlOut as HlOut, Hl = ((
0, N): 1.0)) is set, and the influence of the cause of the failure on other parameters is traced. Therefore, the higher the degree of agreement between the value indicated by the sensor of interest and the corresponding parameter value on the model, the closer the current state of the device is to that model.
In other words, it means that the cause of failure assumed to derive the model is likely to be the current cause of failure.

In this example, HlOut, HlCu
The degree of agreement between the t model and the sensor is 0.9, and both are considered to be the cause. For the above four models,
The ranking is as follows. Ranking 1. A, C: 0.9 2. B, D: 0.63 In response to the case where multiple causes are derived in this way, the priority order of the failure causes is stored in the target model storage unit 15 in FIG. You may also do this. In this embodiment, the following operations are performed to further narrow down the search results.

(ii) Degree of occurrence of phenomenon Taking into account the degree of occurrence of the phenomenon derived at the stage of considering the influence of deterioration in (2), multiply the value indicating the degree of coincidence by the value indicating the degree as shown below. Narrow down your search accordingly. p(HlOut)=1 p(HlCut)=0.9 Rank 1. A: 0.9 x 1.0 = 0.9
Normalize to 1.0 2. C: 0.9 x 0.9 = 0.81 Normalized 0.9 3. B: 0.63×1.0=0.63 Normalized to 0.7 4. D: 0.63 x 0.9 = 0.57 Normalized 0.63 The higher the calculated value of (degree of matching) x (degree), the higher the priority, so it can be seen that HlOut is the most suspicious. Therefore, the repair will be performed by changing the light intensity of the halogen lamp.

Example 3: Example of deterioration after nth month (nT1) of continued use Fuzzy qualitative values of each parameter are determined from actual measured values obtained from the sensor. X=(N:0.8,(0,N):0.2)Vs=(N:
0.9, (N, nil): 0.1) Os = (N: 0.9
, (N, nil): 0.1) (1) Predicting the current state of deterioration By applying nT1 to FIG. 12, Hl=Dn is obtained.

(2) Examining the influence of the amount of deterioration Applying Hl=Dn obtained in (1) above to FIG. 13, we obtain Hl=(N: 0.915, (0, N): 0.085). . Also, if Hl=Dn is applied to FIG. 14, p(H
We obtain lCut)=1.24. (3) Using SSIM, infer the current state of the entire machine using the results of (2). From the results of (2) above and the initial conditions, H
l = (N: 0.915, (0, N): 0.085)D
= (N: 1.0) β = (N: 1.0) Vn = (N: 1.0) Vb = (N: 1.0) γ0 = (N: 1.0) Vt = (N: 1 .0) ζ = (N: 1.0) Asp = (N: 1.0) When SSIM is performed under these conditions, the following results are obtained.

Hl=(N:0.915, (0,N):0.085)D
= (N: 1.0) X = (N: 0.915, (0, N): 0.085)
or (N: 0.915, 0:0.085) β = (N
:1.0) Vs=(N:0.915, (N, nil):0.085
) Vn=(N:1.0) Vb=(N:1.0) γ0=(N:1.0) Ds=(N:0.915, (N, nil):0.085
) Vt = (N: 1.0) ζ = (N: 1.0) Os = (N: 0.915, (N, nil): 0.085
) Asp=(N:1.0) Sp=(N:0.915, (N, nil):0.085
) From this result, it can be seen that the image may become darker (Os increases) as an effect of Hl deterioration. Also,
From the changes in each parameter value, it is generally possible to infer the combined effects of deterioration of multiple parts and, as a result, failures that occur in a cascading manner. (4) Failure determination (a) From the fuzzy qualitative value of machine functional evaluation Os, N=0.9, N≧0
．． Since it is 5, it is normal.

(b) Comparison of model and actual measured values Each sensor value is compared with the model resulting from the above (3) to determine the degree of agreement between the two. A. X=(N:0.915, (0,N):0.
085) Model Matching degree of each sensor X: 0.8 Vs: 0.8 Os: 0.8 Overall 0.87 B. X=(N: 0.915, 0:0.085)
The degree of agreement of each sensor in the model X: 0.8 Vs: 0.9 Os: 0.9 Overall 0.87 Therefore, it can be seen that it seems to match model A or B. In conclusion, the function is normal, and although the machine has deteriorated to the model A or B condition, it is determined that it can be used normally.

Example 4: When a failure occurs under the same conditions as Example 3, the following data is obtained as a sensor value. X = (N: 0.1, (0, N): 0.9) Vs = (
(N: nil): 1.0) Os = ((N: nil): 1.0) Also, by applying the values of Hl and T to Figures 12, 13, and 14, (1) of Example 3 above It is assumed that the same results as in (3) are obtained. (4) Failure determination (a) Functional evaluation of the machine From the fuzzy qualitative value of Os, N=0 and N<0.5, which is abnormal. Therefore, it is judged to be a failure. (5) Derivation of fault candidates The fault candidates stored in advance are the same as those described in (5) of Example 2. Therefore, by tracing Os=(N, nil) on the parameter model shown in Figure 11, HlC
ut HlOut is obtained as a failure candidate.

(6) Fault simulation HlCut using SSIM
, HlOut, we obtain the same result as in Example 2 (6). In addition, in this example, for the sake of simplicity, the explanation is made assuming that only the exposed portion deteriorates, so the failure simulation result of this example 4 is the same as that of example 2.
Generally, the results will not be the same because other parameter values change due to the effects of deterioration. For example, if ζ=(N: 0.8, (0, N): 0.2) as an effect of deterioration of the output section, the following result is obtained for HlOut.

Hl=((0,N):1.0) D=(N:1.0) X=((0,N):1.0)or(0:1.0)β
= (N: 1.0) Vs = ((N, nil): 1.0) Vn = (N: 1.0) Vb = (N: 1.0) γ0 = (N: 1.0) Ds = ((N, nil): 1.0) Vt = (N: 1.0) ζ = (N: 0.8, (0, N): 0.2) Os = (
(0, N): 0.2, N: 0.2, (N, nil): 0
．． 8) Normalize to ((0, N): 0.18, N: 0.18, (
(N, nil): 0.8) Asp = (N: 1.0) Sp = ((N, nil): 1.0) (7) Identification of failure cause Degree of agreement between model and sensor and degree of occurrence of phenomenon 2
From this point on, rank and narrow down the causes of failure.

(i) Degree of agreement between model and sensor A. HlOut X=0
B. HlOut X=(0,N)
X: 0.9
X: 0.1
Vs: 1.0
Vs: 1.0Os
: 1.0
Os: 1.0 overall
0.97 overall
0.7 C. HlCut X=0
D. HlCut X=(0,N)
X: 0.9
X: 0.1
Vs: 1.0
Vs: 1.0Os
: 1.0
Os: 1.0 overall
0.97 overall
0.7 rank 1. A, C: 0.97 2. B, D: 0.7 (i) Degree of occurrence of phenomenon p(HlOut) = 1 p(HlCut) = 1.24 Ranking 1. C: 0.97×1.24=1.20
Normalize to 1.0 2. A: 0.97 x 1.0 = 0.97 Normalized to 0.81 3. D: 0.7 x 1.24 = 0.87 Normalized to 0.73 4. B: 0.7 x 1.0 = 0.7
Normalized to 0.58 Therefore, C is the most suspicious. Also, at least HlCu
t is doubtful. However, since HlCut means that the halogen lamp is burnt out and repair is impossible, no repair is performed.

Note that whether or not repair is possible is determined by dividing the causes of failure into recoverable causes and non-recoverable causes and storing them in the target model storage unit 15 in FIG. 3, and refer to them as appropriate. can do. Furthermore, if the cause of the failure that cannot be repaired is determined with the highest priority, this may be displayed on the display unit.

[0082]

As described above, the present invention provides object model storage means, deterioration data storage means, data input means, deterioration amount calculation means, change calculation means, data conversion means, failure determination means, failure diagnosis means, and causes. Since the configuration includes an identification means, it is possible to automatically perform a failure diagnosis that takes into account the amount of deterioration of the component parts that make up the image forming apparatus.
It is possible to save labor in the repair work of image forming apparatuses and to extend automatic operation for a long period of time.

Furthermore, in order to obtain the amount of deterioration, the relationship between the usage time and the amount of deterioration is stored in advance and the amount of deterioration is determined from that relationship, so the current amount of deterioration can be quickly determined and the accuracy of failure diagnosis increases. In addition, the program for calculating the amount of deterioration may be simple. In addition, the degree of occurrence of failure phenomena that appears according to the amount of deterioration is determined and used when determining the cause of failure, so even if multiple causes of failure are listed, the true cause of failure can be more accurately estimated. can.

Furthermore, when accumulating a deterioration/failure history information base, an accurately classified information base can be created, which can be expected to be used for the development of comprehensive reasoning. In other words, an inference loop can be formed by different types of inference: qualitative inference → case inference → fuzzy inference → qualitative inference.
We can expect to build advanced autonomous systems.

[Brief explanation of the drawing]

FIG. 1 is a diagram showing an example of a qualitative quantity space.

FIG. 2 is a diagram illustrating an example of a membership function that converts sensor information into fuzzy qualitative values.

FIG. 3 is a block diagram showing the configuration of an embodiment of the present invention.

FIG. 4 is a diagram showing the relationship between usage time and the deterioration state of parts.

FIG. 5 is a diagram showing an example of a qualitative quantity space of parameters.

FIG. 6 is a diagram showing the relationship between the state of deterioration and the degree of occurrence of the phenomenon.

FIG. 7 is a part of a flowchart showing an inference procedure for fault diagnosis in this embodiment.

FIG. 8 is a part of a flowchart showing an inference procedure for fault diagnosis in this embodiment.

FIG. 9 is a part of a flowchart showing an inference procedure for fault diagnosis in this embodiment.

FIG. 10 is an illustrative diagram of a small electrophotographic copying machine as a specific target machine.

FIG. 11 is a diagram showing a parameter model of the electrophotographic copying machine according to this embodiment.

FIG. 12 is a diagram showing the relationship between usage time and deterioration state in parameter H1.

FIG. 13 is a diagram showing an example of a qualitative fuzzy quantity space of parameters Hl.

FIG. 14 is a diagram showing the relationship between the deterioration state of the parameter Hl and the degree of occurrence of the phenomenon.

FIG. 15 is a diagram illustrating a state in which the cause of a functional abnormality in which Os is large is traced on a parameter model in order to derive a failure candidate.

[Explanation of symbols]

1a, 1b, 1c Sensor 6a, 6b, 6c Actuator 11
Digital signal/FQ value conversion section 12 Deterioration data calculation section 13 Short-term simulation section

Claims (1)

[Claims] 1. A self-diagnosis system for an image forming apparatus that embodies image data to generate a visible image, the image forming apparatus being expressed as a combination of a plurality of elements,
Target model storage means that stores qualitative data that qualitatively represents the behavior or attributes of each element and the coupling relationship between each element using parameters, membership functions of each parameter, failure phenomenon occurrence reference values, and failure diagnosis knowledge. , a deterioration data storage means in which deterioration data representing a relationship between usage time and deterioration amount is stored for predetermined parts constituting the image forming apparatus, a data input means into which arbitrary data can be input, and a deterioration data storage. A deterioration amount calculation means that calculates the amount of deterioration with respect to the usage time up to now based on the deterioration data stored in the means; The value of the parameter that changes depending on the amount of deterioration is expressed as a fuzzy qualitative value using the membership function of the parameter, and the degree of occurrence of a failure phenomenon that appears in the image forming apparatus due to the amount of deterioration is expressed using the failure phenomenon occurrence reference value. A change calculation means for calculating, a data conversion means for converting the data given from the data input means into a fuzzy qualitative value using a membership function of parameters stored in the target model storage means, and a target model storage for the conversion output of the data conversion means. Failure determination means for determining the presence or absence of a failure by comparing with qualitative data stored in the means; in response to the failure determination means determining the presence of a failure, storage of the target model using the calculation result of the change calculation means as an initial condition; A fault diagnosis means that diagnoses the state of the image forming apparatus based on the qualitative data and fault diagnosis knowledge stored in the means, and outputs the diagnosis result in an expression including ambiguity, and a fuzzy qualitative value converted by the data conversion means. a cause identifying means for identifying a cause based on a comparison with the state of the image forming apparatus diagnosed by a failure diagnosing means and a degree of occurrence of a failure phenomenon appearing in the apparatus calculated by a change calculating means; Diagnostic system.