JPH0388031A - Abnormality diagnosis device - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

DETAILED DESCRIPTION OF THE INVENTION Field of the Invention The present invention relates to an apparatus for diagnosing abnormalities in equipment such as machine tools, printers, sewing machines, etc., and particularly relates to improving its flexibility.

[Prior art] Using a computer to determine the causes and countermeasures for abnormalities that occur in equipment, as described in JP-A-63-32650 and JP-A-62-4633, requires the use of manufacturing equipment and This has already been done in vehicles, etc. Such an abnormality diagnosis device includes (a) abnormal symptom input means for inputting abnormal symptoms of the equipment to be diagnosed, and (b) abnormal causes, abnormality countermeasures, etc. that cause the abnormal symptoms inputted from the abnormal symptom input means. (C) based on the abnormality diagnosis information stored in the abnormality diagnosis information storage means, which stores abnormality diagnosis information necessary for diagnosing the abnormality in association with abnormal symptoms;
The apparatus is configured to include (d) an inference means for inferring at least one of an abnormality cause and an abnormality countermeasure corresponding to the abnormal symptom inputted from the abnormal symptom input means, and (d) an output means for outputting the result of the inference. If only one of the cause of the abnormality and the countermeasure for the abnormality is stored in the abnormality diagnosis information, the stored cause of the abnormality or the stored abnormality countermeasure is inferred. In addition, if both are stored, the cause of the abnormality may be inferred and a countermeasure for the abnormality may be inferred based on that inference. There is also. In any case, if the abnormality diagnosis information necessary for diagnosing an abnormality, such as the cause and countermeasure for the abnormal symptom, is stored in the storage means, at least one of the abnormality cause and abnormality countermeasure can be inferred based on the input of the abnormal symptom. , the diagnostician can take appropriate measures based on the diagnostic results, and can respond to abnormalities more quickly than if they had to check maintenance manuals etc. one by one and take countermeasures. I can do it.

In this way, abnormality diagnosis by an abnormality diagnostic device is performed using input, inference, and output, but each of these inputs, inferences, and outputs includes one or more execution items, and conventionally, these are executed according to a diagnostic program. It was becoming like this. A diagnostic program is a program in which a plurality of execution items are arranged in a predetermined order, and has the function of fixedly defining the execution items themselves and the order in which they are executed.

Problems to be Solved by the Invention However, when a diagnostic program fixedly defines a plurality of execution items and their execution order, the execution items themselves may be changed due to changes in the equipment to be diagnosed for abnormality, etc. or if you need to change the execution order,
There was a problem in that the entire diagnostic program had to be rewritten, which was time-consuming.

The present invention has been made with the object of providing a highly flexible abnormality diagnosis device that can easily change the execution items and execution order of abnormality diagnosis.

Means for Solving the Problems In order to solve the above problems, the present invention, as shown in FIG. and (d) in an abnormality diagnosis device equipped with an output means, for at least one of input, inference, and output, specific execution items are separated from the main program that executes them, and subprograms are created for each execution item. an abnormality diagnosis strategy storage means for individually storing the execution items in the execution item storage means and the execution order of the execution items selected from the execution items, and storing the main program in the abnormality diagnosis strategy storage means. The present invention is characterized in that the subprograms stored in the execution item storage means are executed sequentially in accordance with the order of execution.

Note that the term "abnormality" as used herein includes not only failures that require repair of the equipment, but also situations that can be resolved without repair, such as operational errors and poor adjustment.

Function The abnormality diagnosis device of the present invention has three stages of abnormality diagnosis:
That is, at least one stage of input, inference, and output is performed according to the main program, the abnormality diagnosis strategy information, and the subprogram. Selecting an item that is currently desired to be executed from among execution items that can be executed by executing a plurality of subprograms stored in the execution item storage means,
By storing the desired order of execution in the abnormality diagnosis strategy storage means, abnormality diagnosis strategy information is completed, and by the main program executing some of the subprograms according to this abnormality diagnosis strategy information, the above three steps can be completed. At least one step is performed.

Moreover, if the combination or order of the execution items stored in the abnormality diagnosis strategy information, that is, the abnormality diagnosis strategy storage means, is changed, the execution content of at least one stage is changed.

The abnormality diagnosis strategy information may be created or changed by the manufacturer of the abnormality diagnosis device or by the user. If a manufacturer does this, it will be possible to easily manufacture various abnormality diagnostic devices, and if a user does this, the device will be a highly versatile abnormality diagnostic device whose functions can be changed according to the purpose of use.

Effects of the Invention As described above, the abnormality diagnosis device according to the present invention can improve the input, inference, and output for abnormality diagnosis by changing the execution items themselves or the order in which they are stored in the abnormality diagnosis strategy storage means. The content of at least one step can be easily changed.

Changing this abnormality diagnosis strategy information is extremely easy, and there is no need to change the main program or subprograms, making diagnosis much easier and faster than the conventional method of re-creating the entire diagnostic program. It becomes possible to change the contents.

Furthermore, since the subprograms for execution items are individually stored in the execution item storage means, their contents can be easily modified.

EXAMPLE Hereinafter, a case in which the present invention is applied to an abnormality diagnosis device for a machine tool will be explained in detail based on the drawings.

This abnormality diagnosis device is installed in a location separate from the machine tool, and performs abnormality diagnosis in response to a telephone call or the like from a user. As shown in FIG. 2, the control device 10 of this abnormality diagnosis device includes a CPU 12. The main body of the computer is a ROM 14, a RAM 16, 17, and a bus 18 that connects them.

An input interface 20 is connected to the bus 18, and an input device 22 is connected to the input interface 20. The input device 22 is used by a diagnostician to input abnormal symptoms and the like. Bus 18 also includes output interface 2.
8 is connected to the output interface 28, and the drive circuit 3 is connected to the output interface 28.
A display device 32 is connected via 0.

The RAM 16 is a backup RAM whose memory contents do not disappear even when the power of the abnormality diagnosis device is turned off, and as shown in FIG. It has an inference memory, an inference memory for boat fishing abnormality diagnosis information, an input program memory, an output program memory, an abnormality diagnosis strategy memory, a question data memory for inputting abnormal symptoms, a data memory for verifying cause candidates, and the like. Further, as shown in FIG. 4, the RAM 17 is provided with an abnormal symptom memory, an abnormal cause memory, a counter, etc. together with a working memory.

The unique abnormality diagnosis information memory stores abnormality diagnosis information unique to each machine tool. The abnormality diagnosis information consists of three types of information: failure information, connection information, and structure information. As shown in FIG. 6(a), the failure information includes abnormal symptoms, corresponding abnormal causes, and abnormal countermeasures, and is created based on the configuration of each machine tool and the like in advance. Further, the connection information is mechanical as shown in FIG. 6(b).

Components that have some kind of electrical or other connection in each machine tool are paired together, and the structural information indicates the structure of each machine tool in a certain way, as shown in Figure 6 (C). This is information that hierarchically relates one component to the components that make up that component.

The general abnormality diagnosis information memory stores abnormality diagnosis information that generally occurs in a large number of machine tools. This - boat fishing abnormality diagnosis information includes environmental information, which is the symptoms, causes, and countermeasures for abnormalities that occur based on the environment in which the machine tool is installed, as shown in Figure 7 (a), and environmental information as shown in Figure 7 (b). As shown, it includes operation information that is the symptoms, causes, and countermeasures for abnormalities that occur based on operations.

Inference of abnormality causes and abnormality countermeasures for abnormal symptoms is performed based on the above-mentioned unique abnormality diagnosis information and general abnormality diagnosis information, and the unique abnormality diagnosis information inference memory stores three types of unique abnormalities as shown in Fig. 8. A program for inference using each of the diagnostic information is stored, and a program for inference using each of two types of general abnormality diagnosis information is stored in the boat fishing abnormality diagnosis information inference memory, as shown in FIG. has been done. Of these, a program for inference using failure information is shown in FIG. The other four reasoning brodarums are:
In the first step of the program shown in FIG. 10, "failure information" is "connection information".

The only changes are ``structural information,'' ``environmental information,'' and ``operation information,'' and the rest is the same. Note that the inference of the cause of the abnormality is performed by searching for the cause of the abnormality based on the abnormality diagnosis information and then verifying the cause of the abnormality. This verification is performed by interviewing the diagnostician, and verification question data is stored in the cause candidate verification data memory.

The input program memory stores three types of input programs as shown in FIG. Test data input is the input of abnormal symptoms when testing whether the abnormality diagnosis device operates normally, and online data input is used for abnormality diagnosis when the abnormality diagnosis device is directly connected to the equipment to be diagnosed. This is a format in which abnormal symptoms are directly input from the target device. The operator input is in the form of inputting abnormal symptoms by interviewing the diagnostician, and the abnormal symptoms are input by displaying questions on the display device 32 and inputting answers by the diagnostician. For this purpose, questions for inputting abnormal symptoms are stored in the abnormal symptom input question data memory. Input of abnormal symptoms in these three types of formats is performed according to different programs, and these programs are stored in the input program memory.

The operator input program is shown in flowchart form in FIG.

Furthermore, the output program memory stores four types of output programs as shown in FIG. Screen output is a format in which the result of inference is displayed on a CRT display, and online output is a format in which the result of inference is output to a device to be diagnosed for abnormality. Printer output is a format that prints the inference results, and screen output with certainty is a format that outputs the inference results to the screen with a probability that the abnormality will be resolved based on the results. are performed according to different programs, each of which is stored in an output program memory.

Further, the abnormality diagnosis strategy memory stores an abnormality diagnosis execution procedure. This execution procedure is based on the execution items, namely 3.
It specifies the execution items themselves and their execution order that are selected from among the types of input formats, five types of inference modes, and four types of output formats and are actually used for abnormality diagnosis,
The storage location of the program for the execution item is stored in the abnormality diagnosis strategy memory in association with each count value of the counter. In this abnormality diagnosis device, abnormality diagnosis is performed according to the abnormality diagnosis strategy shown in FIG.

The ROM 14 stores an abnormality diagnosis execution program shown in a flowchart in FIG. Hereinafter, based on this flowchart, abnormality diagnosis using the abnormality diagnosis strategy shown in FIG. 14 will be explained.

First, step Sl (hereinafter abbreviated as SI) is performed.

The same goes for other steps. After the count value C of the counter is set to 1 in ), it is determined in S2 whether there is an execution item corresponding to C=1. The execution item corresponding to the count value 1 is searched based on the stored contents of the abnormality diagnosis strategy, and since the count value 1 is associated with the operator input, 32&:! , YES, and the execution item corresponding to the count value 1 is executed in S3. That is, operator input is performed according to the program shown in the flowchart of FIG. Hereinafter, by repeatedly executing the program shown in the flowchart of FIG. 5 while increasing the count value by 1, inference and output are performed according to the abnormality diagnosis strategy shown in FIG. 14. The figures are summarized in one flowchart.

First, operator input is performed as follows.

In 5101, data is read from the abnormal symptom input question data memory, and in 3102, it is determined whether the data is data that instructs the end of the question. Initially, this determination is NO, and the question read out in 3103 is displayed on the display device 32. If the answer to the question is input, 3104 becomes YES and 510
In step 5, the abnormal symptoms are stored in memory.

When the input of answers to all the questions stored in the abnormal symptom input question data memory is completed, 5102 becomes YES, the program execution returns to the execution program shown in FIG. 5, S4 is executed, and the count value of the counter is increased. Increased by 1. Next, S2 is executed, but the determination is YES, and in S3, the execution item corresponding to the count value 2, that is, the cause of the abnormality and the countermeasure are inferred based on the failure information. This inference is performed by executing the program shown in the flowchart of FIG. 10, and this execution is shown in the flowchart of FIG. 15 following the input of the abnormal symptoms.

In 3106, the cause of the abnormality and the countermeasure for the abnormality are searched based on the failure information, and if there is a cause and countermeasure for the abnormality corresponding to the abnormal symptom stored in the abnormal symptom memory, 5107 is returned as Y.
ES and verification of cause candidates is performed in 310B. This verification is performed by interviewing the diagnostician as described above, and if it is determined that the cause of the abnormality has occurred, 3109 becomes YES, and in 3110, the cause of the abnormality and the corresponding abnormality countermeasure are stored in the abnormality cause memory. After being stored, S4 of the execution program is performed.

In addition, if there is no abnormal cause corresponding to the abnormal symptom as a result of the search, or if the occurrence of all abnormal causes is denied as a result of verification of abnormal cause candidates, 5107,310
9 becomes No, and 84 of the execution program is executed.

Next, the items specified by the count value 3 are executed, and the cause of the abnormality and the abnormality countermeasure are searched and verified based on the structure information (this search and verification are steps 5111 to 5115 in the flowchart of FIG. 15).

In this case, as a result of the verification, if it is determined that the cause of the abnormality has occurred, the cause of the abnormality and the countermeasure for the abnormality are stored in the abnormality cause memory. Next, the item specified by count value 4 is executed, and after the cause of the abnormality is inferred based on the environmental information (this inference is from 5116 to 5120 in the above flowchart), the item specified by count value 5 is executed. The cause of the abnormality is inferred based on the operation information (this inference is in steps 5121 to 5125 in the above flowchart).

When this inference is completed, the next thing specified by the count value 6 is a screen output with certainty, which is displayed on the display device 32 if the abnormality cause and countermeasure are stored in the abnormality cause memory (this display is 5126 in the flowchart).

At this time, regarding the cause of the abnormality, the probability of occurrence stored in advance together with the cause of the abnormality is read out and displayed together. Further, if there is no cause of the abnormality, a message indicating that there is no cause of the abnormality is displayed.

Next, the items specified by the count value 7, that is, output to the printer are executed (shown in S127), and the cause of the abnormality and the countermeasures for the abnormality are printed. When the count value reaches 8, 32 of the diagnostic program becomes NO, so the execution program ends, and a series of abnormality diagnosis of input, inference, and output ends.

As described above, in this abnormality diagnosis apparatus, abnormality diagnosis is performed by executing the execution items constituting the abnormality diagnosis strategy according to the execution program shown in FIG. 5. However, when changing the abnormality diagnosis strategy, input is required.

What is necessary is to select the items to be used from the respective execution items of inference and output, and store them in the abnormality diagnosis strategy memory in association with the count value of the counter.

In addition, multiple inference programs, input programs, and output programs are each stored independently in memory, so if a program needs to be modified, it is only necessary to extract and modify the necessary program, making it easy to modify. be able to.

As is clear from the above description, in this embodiment, the input device 22 serves as an abnormal symptom input means, and the display device 32 serves as an abnormal symptom input means.
constitutes an output means, a unique abnormality diagnosis information memory and a general abnormality diagnosis information memory constitute an abnormality diagnosis information storage means, a unique abnormality diagnosis information inference memory, - boat fishing abnormality diagnosis information inference memory, an input program memory and the output program memory constitutes an execution item storage means, the abnormality diagnosis strategy memory constitutes an abnormality diagnosis IFr strategy storage means, and the ROM14
The part that stores 33 and the part that performs inference based on the program stored in the specific abnormality diagnosis information inference memory and the program stored in the general abnormality diagnosis information inference memory of the CPU 12 constitute the inference means. .

Note that the abnormality diagnosis strategy information is if (condition) then
(Execution action) It may be configured by a production rule written in the form of (execution action). For example, "If the cause of the abnormality is determined based on the failure information (if), output (then)"
Create the rules necessary for abnormality diagnosis, such as the rule ``If the cause of the abnormality is determined based on the connection information, output (then) J'' and execute this generation rule in the main program. In this case, out of multiple types of information, if the cause of an abnormality is found for -type of information, a rule is created so that no inference is made about other information, or a rule is created so that inference is not made about all the information. However, if the cause of an abnormality cannot be determined, a rule can be created to re-input the abnormal symptoms, so that the diagnosis actually performed according to the abnormality diagnosis strategy information can be made in a different manner depending on the case.

In addition, in the abnormality diagnosis device of the above embodiment, information that is not unique to each machine tool, that is, the main program shown in FIG. 5, the subprograms shown in FIGS. It can be used as is when creating an abnormality diagnosis device for machine tools. Also,
If the input format and output format of another machine tool are executable with the programs stored in the input program memory and output program memory, they can be used.

Furthermore, when outputting the inferred abnormality cause with a confidence level attached, the confidence level may be determined and attached based on the verification result. Among the symptoms to verify the cause of the abnormality,
The degree of certainty is determined and assigned based on how many symptoms are confirmed or denied.

Furthermore, the abnormality diagnosis information may be such that only the cause of the abnormality is inferred, and a diagnostician or the like may consider or investigate the abnormality countermeasures based on the inferred cause of the abnormality.

Further, in the above embodiment, abnormality diagnosis strategy information is created for each of input, inference, and output, but it may be created for any one or two of them.

Furthermore, the present invention can also be applied to devices for diagnosing abnormalities in equipment other than machine tools.

In addition, the present invention can be implemented with various modifications and improvements based on the knowledge of those skilled in the art without departing from the scope of the claims.

[Brief explanation of drawings]

FIG. 1 is a block diagram conceptually showing the configuration of the present invention. FIG. 2 is a block diagram of an abnormality diagnosis device that is an embodiment of the present invention. FIG. 3 is a diagram showing the configuration of a backup RAM of the computer that controls the main body of the abnormality diagnosis apparatus, and FIG. 4 is a diagram showing the configuration of the RAM. FIG. 5 is a flowchart showing the main program stored in the ROM of the computer. FIG. 6(a) is a table showing an example of failure information among the unique abnormality diagnosis information stored in the unique abnormality diagnosis information memory provided in the separate-up RAM, and FIG. 6(b) is a table showing connection information. FIG. 6(C) is a table showing an example of structure information. FIG. 7(a) is a table showing an example of environmental information among the general abnormality diagnosis information stored in the general abnormality diagnosis information memory provided in the backup RAM, and FIG. 7(b)
is a table showing an example of operation information. FIG. 8 is a table showing the contents of the specific abnormality diagnosis information inference memory provided in the backup RAM, and FIG. 9 is a table showing the contents of the general abnormality diagnosis information inference memory. FIG. 10 is a flowchart showing an inference program (subprogram) based on the failure information stored in the specific abnormality diagnosis information inference memory. FIG. 11 is a table showing the contents of the input program memory provided in the backup RAM, and FIG. 12 is a flowchart showing the input program (subprogram) stored in the input program memory. FIG. 13 is a table showing the contents of the output program memory of the bank-up RAM. FIG. 14 is a table showing abnormality diagnosis strategy information stored in the abnormality diagnosis strategy memory provided in the backup RAM. FIG. 15 is a flowchart showing a program when abnormality diagnosis is performed according to the above abnormality diagnosis strategy information. 10: Control device 22: Input device 32: Display device Fig. 1 Fig. 3 Fig. 4! @5 Figure 6 (a) (b) (C) Figure 7 (a) (b) Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14

Claims (1)

[Scope of Claims] Abnormal symptom input means for inputting abnormal symptoms of the equipment to be diagnosed, and abnormalities necessary for diagnosing the abnormality, such as abnormal causes causing the abnormal symptoms and abnormal countermeasures inputted from the abnormal symptom input means. an abnormality diagnosis information storage means for storing diagnostic information in association with abnormal symptoms; and an abnormality corresponding to the abnormal symptom input from the abnormal symptom input means based on the abnormality diagnosis information stored in the abnormality diagnosis information storage means. In an abnormality diagnosis device having an inference means for inferring at least one of a cause and an abnormality countermeasure, and an output means for outputting a result of the inference, the above-mentioned at least one of the input, inference, and output is executed. An abnormality diagnosis strategy in which specific execution items are separated from the main program, subprograms for each execution item are individually stored in the execution item storage means, and the execution order of the execution items selected from these execution items is stored. An abnormality diagnosing device comprising a storage means, and the main program is sequentially executed by the sub-programs stored in the execution item storage means according to the execution order stored in the abnormality diagnosis strategy storage means. .