JP3461932B2 - Fault diagnosis device using expert system - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention, when an equipment such as an elevator or an escalator installed in a station premises fails, or before a failure occurs, a failure occurrence portion, a failure occurrence portion, or a failure occurrence time can be determined. The present invention relates to a failure diagnosis device using an expert system that estimates and informs an operator or a maintenance worker of a site where a failure has occurred and assists in promptly taking countermeasures.

[0002]

2. Description of the Related Art FIG. 6 is a block diagram showing the configuration of a conventional fault diagnosis apparatus using an expert system.
In the figure, 1 is a target machine facility (hereinafter referred to as target facility), 2 is a knowledge database that stores diagnostic rules for the target facility 1 using a knowledge expression such as a rule base, and 3 is online from the target facility 1 The input monitoring information, 4 is an inference engine for performing a failure diagnosis by using the monitoring information 3 and the knowledge database 2, and 5 is a display of the failure diagnosis result inferred by the inference engine 4 and the input of data for constructing the knowledge database 2. It is a man-machine device for.

Next, the operation will be described. Now, when a failure occurs in the target equipment 1, the monitoring information 3 changes. The inference engine 4 matches the predetermined monitoring information 3 with the knowledge database 2 to identify the faulty part of the target equipment 1 and output the diagnosis result to the man-machine device 5. Also,
The inference engine 4 operates by registering in advance the know-how related to failure diagnosis of an expert (skilled person) from the man-machine device 5 as a rule base in the knowledge database 2.

[0004]

The conventional failure diagnosis device using the expert system is constructed as described above, and the expert system is used to carry out the failure diagnosis based on the result of the failure, and the diagnosis result is displayed. Since it is provided to operators and maintenance workers, it is effective in reducing the time from the occurrence of a failure to the recovery of the failure, so-called downtime, while the function as preventive maintenance to prevent the failure is provided. There is a problem in that it cannot be used for preventive maintenance in order to prevent failures before it has it.

The present invention has been made in order to solve the above-mentioned problems, and a failure diagnosis apparatus using an expert system capable of providing failure prediction information about a target facility to an operator before a failure occurs. Aim to get.

Another object of the present invention is to obtain a failure diagnosis device using an expert system that can simplify the system configuration.

[0007]

A failure diagnosis apparatus utilizing an expert system according to the invention of claim 1 is a target device.
Current phenomenon based on the monitoring information obtained from the equipment
The judgment type to be applied to
The above-mentioned phenomenon changes with the progress of diagnostic rules and time.
The state of the phenomenon change in the case, the degree to the abnormal state, the future
The type of judgment applied to the accuracy of abnormality occurrence in
And a prediction rule that consists of
Based on the judgment type and the judgment condition and the judgment rule defined by the judgment condition defining means, and the failure diagnosis and the failure occurrence at the time of the failure of each part of the target equipment. and a determining failure factor determining means the failure factor performed before failure prediction, before
Monitoring the target equipment as the judgment type and its judgment conditions
Good to apply for diagnosis based on binary information
Conditions and upper and lower limits for making a pass / fail judgment
Applied to make a diagnosis based on threshold information that defines
And the conditions for performing upper and lower limit judgments and upper and lower limit judgments,
Fuzzy applied to make diagnosis based on fuzzy function
Judgment and its fuzzy judgment conditions and human perception
The stage judgment applied to make a diagnosis based on the objective criteria.
And the conditions for determining the stage and the passage of time.
Trend judgment applied to make a diagnosis based on the tendency to change
Conditions and the conditions for determining the tendency and the current occurrence
Diagnosis based on the amount of difference between the existing phenomenon and the phenomenon that occurred in the past
Perform the difference judgment applied to do and the difference judgment
It is provided with the conditions of the occasion .

A failure diagnosis apparatus using an expert system according to a second aspect of the present invention is an operation system for a target equipment.
Data, test data obtained by performing inspections
With a configuration to collect which monitoring information online
It is a thing.

According to a third aspect of the present invention, there is provided a failure diagnosis device using an expert system, which is an operation system for a target facility.
Data, test data obtained by performing inspections
With a configuration that collects which monitoring information offline
It is a thing.

[0010]

According to the invention as set forth in claim 1, the fault diagnosis apparatus utilizing the expert system can cope with the phenomenon which is currently occurring.
Diagnosis that consists of the judgment type and the judgment conditions to be applied
If the above-mentioned phenomenon changes over time with the disconnection rule
The degree of change in the phenomenon of
And the judgment for determining the accuracy of the abnormality.
In advance of the prediction rule consisting of fixed types and their judgment conditions
Definition of monitoring information obtained from the target equipment , failure diagnosis at the time of failure occurrence and failure prediction before failure occurrence for each part of the target equipment based on the defined judgment type and its judgment conditions and judgment rules. The failure factor is determined by performing monitoring information as the determination type and the determination conditions.
And the pass / fail judgment for diagnosing based on binary information and
Based on the judgment condition and threshold information that defines the upper and lower limits
Diagnosis of the degree of approach to a condition considered abnormal
Upper and lower limit judgment and its judgment conditions for performing, and fuzzy
To diagnose the accuracy of anomalies based on functions
Fuzzy judgment and its judgment conditions, and the sense of human senses
Stage judgment for making a diagnosis based on conscious criteria and its
Judgment conditions and the time required to reach the abnormal occurrence state
Diagnosis is based on the trend of monitoring data that changes with time
And trends determination and the determination conditions for, not currently occurring
Diagnosis based on the difference between the phenomenon that occurs and the phenomenon that occurred in the past.
Combination of difference judgment and its judgment condition for
It is possible to perform failure diagnosis and failure prediction on a target facility and provide the operator with failure prediction information of a site that is expected to fail before a failure occurs.

A failure diagnosis apparatus using an expert system according to a second aspect of the present invention is an operation related to a target equipment.
Data / fault data and tests detected by inspection
Collecting monitoring information such as data online
To efficiently collect monitoring information and
A combination of diagnosis and failure prediction is expected to cause a failure.
The failure prediction information of the
It is possible to provide to the data.

A failure diagnosis apparatus using an expert system according to a third aspect of the present invention is an operation related to a target equipment.
Data, failure data and test data obtained by conducting inspections.
Monitoring information such as data is collected off-line for failure diagnosis.
Disconnection and failure prediction, offline system configuration
Realization of simplification, failure of parts that are expected to fail
Providing predictive information to the operator before a failure occurs
Noh.

[0013]

EXAMPLES Example 1. An embodiment of the present invention will be described below with reference to the drawings. Figure 1
FIG. 1 is a block diagram showing a configuration of a failure diagnosis device using an expert system according to a first embodiment of the present invention.
In FIG. 1, parts that are the same as or correspond to those in FIG. 6 are given the same reference numerals and description thereof is omitted. In the figure, 6 is a diagnostic rule for determining whether or not an abnormality has occurred in a portion corresponding to the monitoring information based on the monitoring information, and 7 is a prediction rule for predicting a failure occurrence based on the monitoring information.
Reference numeral 8 is an online system for collecting monitoring information online from the target facility 1. The diagnosis rule 6 and the prediction rule 7 correspond to the judgment rule and the failure factor judgment means, the inference engine 4 corresponds to the failure factor judgment means, and the man-machine device 5 corresponds to the judgment condition definition means.

FIG. 2 is a diagnosis / prediction definition table for defining diagnosis / prediction conditions as to what kind of judgment is applied to each piece of monitoring information and the judgment conditions to apply the failure diagnosis prediction. Monitoring information S1 obtained from the equipment 1
The definition contents of the determination type and the determination condition applied to each of S2, S3, ... Are set. This definition content is a pass / fail judgment that determines whether the monitor information is good or bad, a degree of change of the monitor information is determined, and a tendency determination that predicts a period until reaching a state considered abnormal, A difference judgment for observing the degree of deterioration based on the difference amount from the previous time in the monitoring information and making a judgment to detect abnormalities early, a fuzzy judgment that calculates the probability of occurrence of an abnormality based on a fuzzy function using fuzzy theory, There are upper and lower limit judgments that judge the degree to which the monitoring information is approaching abnormal based on the upper and lower limit values that have been set, and stage judgments that judge the monitoring information based on the human senses. It is set based on the know-how obtained from

FIG. 3 is a causal relationship table in which the relationship between the diagnosis / prediction conditions and the estimated failure site is defined in a matrix structure. Based on the know-how that an expert has gained from many years of experience, the judgment type And the determination condition and the like in the determination type are set in association with the estimated failure site.
These diagnostic prediction definition table and causal relationship table are registered in the knowledge database 2 as diagnostic rules 6 and prediction rules 7.

In the diagnosis / prediction definition table shown in FIG. 2, diagnosis / prediction definition items (various judgment types such as pass / fail judgment and tendency judgment) to be applied to each monitoring information such as S1, S2, S3, ... ) Is defined, the quality determination is applied to the monitoring information S1, and the tendency determination and the difference determination are applied to the monitoring information S2.

Next, the operation will be described. In this case, elevator failure monitoring will be described as an example. First, as a diagnostic rule 6 and a predictive rule 7 by a so-called expert system, which utilizes the know-how that an expert has gained from many years of experience, as shown in FIG. The quality item is defined as a definition item, and the tendency determination and the difference determination are defined for the measurement data S2 that is the monitoring information of the elevator. Next, the quality judgment of the elevator failure data S1 is defined by one diagnosis / prediction condition A in the causal relationship table shown in FIG. 3, and "a" is estimated as a failure part estimated when a phenomenon satisfying this diagnosis / prediction condition occurs. Is defined.

When a failure occurs in the elevator of the target equipment 1, the failure data S1 of the monitoring information 3 collected by the online system 8 is changed, the inference engine 4 is activated, and the inference is made by the diagnosis rule 6 of the knowledge database 2. Then, “a” is extracted as the estimated failure part and displayed on the man-machine device 5.

Similarly, the elevator measurement data S2 is always observed and taken into the inference engine 4 as the monitoring information 3. If there is a change in this data, the inference engine 4 is started and the inference is made by the prediction rule 7 of the knowledge database 2. The estimated failure part “b” and the failure occurrence prediction time are extracted and displayed on the man-machine device 5. As the predicted occurrence time of the failure, the tendency and difference amount of the measurement data S2, which changes with time of the monitoring information 3 that is constantly observed, are determined by the trend determination and the difference determination, and the timing of the failure occurrence is predicted. It is determined.

FIG. 4 is an explanatory view showing a display screen showing the diagnosis and prediction result displayed on the man-machine device 5, and in this case, the monitoring information name 11 (S2 in this case) which is the factor of the diagnosis and prediction judgment. The failure part 12 (in this case, part "b") inferred as a result of the diagnosis prediction is displayed. Furthermore, according to the prediction rule 7, the diagnosis / prediction condition B for the tendency determination
When it becomes clear that the tendency of the monitoring information S2 satisfies
As the prediction information, the recommended replacement time 13 of the parts and the state of the monitoring information used for the prediction that changes with time are displayed as a graph 14.

Therefore, according to the first embodiment, not only the location of the failure that has occurred by the expert system is specified, but also the appearance of the failure that may occur in the future in the specific area of the target equipment from the monitoring information such as measurement data. It is possible to present the operator with the failure prediction information such as the time and the monitoring information used for the failure occurrence prediction.

Further, in the embodiment described above, the judgment type and the judgment condition for the phenomenon which may occur in the target equipment are defined on the matrix by the matrix structure based on the know-how of the skilled person. Even if the operator is not accustomed to the operation of the engine 4, it is possible to easily change the determination type and the determination condition shown in FIGS. 2 and 3 by using the man-machine device 5.

Example 2. In the first embodiment, the monitoring information 3 is fetched from the target equipment 1 online, but an expert system capable of predicting failure diagnosis is used even if, for example, inspection data is collected offline and fetched as the monitoring information 3. It is possible to construct a fault diagnosis device that has

FIG. 5 is a block diagram showing the structure of a failure diagnosis apparatus using the expert system according to the second embodiment. 5, parts that are the same as or equivalent to those in FIG. In the figure, reference numeral 21 is a data collection device for collecting monitoring information off-line, for example, a pen computer. In this data collection device 21, for example, the operator reads the state of each part of the target equipment from a state display device that detects and displays the state of each part of the target equipment, and inputs it by pen input or the like.

Reference numeral 22 is an input device for inputting monitoring information such as inspection data collected by the data collecting device 21 through a storage medium such as an IC card or a floppy disk.

Therefore, in the second embodiment, not only the failure part is specified by the expert system, but also the appearance time of the failure which may occur in the future in the specific part of the target equipment from the monitoring information such as the measurement data collected offline. Alternatively, it is possible to present the operator with failure prediction information such as monitoring information used for the failure occurrence prediction.

Example 3. Although the diagnosis rule 6 and the prediction rule 7 are provided as the knowledge database in the first embodiment, the failure diagnosis prediction is substantially performed by incorporating only the diagnosis rule 6 as in the case where the prediction rule 7 is incorporated. It is also possible to configure a failure diagnosis device using an expert system that enables the above.

A failure diagnosis apparatus using the expert system of this embodiment is defined by the upper and lower limits in the upper and lower limits determination, for example, which determines the monitoring information based on the set upper and lower limits. The range is set as a range in which a failure is considered to occur after a certain period of time, and it is determined from the monitoring information such as inspection data whether the inspection data is within the range, and the time of future failure occurrence. Can be predicted, and since the prediction rule is not provided, the system configuration can be simplified.

Example 4. In the first embodiment, the knowledge database 2 has the diagnosis rule 6 and the prediction rule 7, but
By incorporating only the prediction rule 7 in the knowledge database 2, a failure diagnosis device using an expert system that enables failure diagnosis of failure prediction or intermediate status that is shifting to a status considered as failure is configured. It is also possible to simplify the system configuration.

[0030]

As described above, according to the first aspect of the invention, the judgment type and
The diagnostic rules that consist of
It is applied to the phenomenon change when the above phenomenon changes accordingly.
Prediction rule consisting of judgment type and judgment conditions
A matrix for the monitoring information obtained from the target equipment
A determination condition defining means for defining the structure, the said determination type and based on and the determination rule the determination condition defined by the determination condition defining means, fault diagnosis and failure when a fault occurs for each part of the object equipment and a determining failure factor determining means the failure factor performs failure prediction before occurrence, determine
As a fixed type and its judgment condition, pass / fail judgment and its
Conditions for making pass / fail judgments, upper and lower limit judgments, and their upper and lower limits
Conditions for limit judgment, fuzzy judgment and its fuzzy judgment
Conditions for making a Gee Judgment, and the stage judgment and its stage judgment
Conditions and the trend judgment and the trend judgment.
Conditions and the difference judgment and the difference judgment
Since it is configured to include conditions , failure prediction information about abnormalities such as failures that occur in the target equipment can be obtained by using the pass / fail judgment, upper / lower limit judgment, fuzzy judgment, step judgment, tendency judgment, difference judgment, etc. There is an effect that a failure diagnosis device using an expert system that can be provided to an operator before a failure occurs can be obtained.

According to the invention of claim 2, it relates to the target equipment.
Obtained by performing operational data, failure data, and inspection
Collect monitoring information such as test data online
Configured to collect the monitoring information for the operator.
Monitoring information efficiently collected without burdening the
More quickly predict failure information about target equipment before a failure occurs.
An expert system that can be easily provided to operators
There is an effect that the failure diagnosis device used can be obtained.

According to the invention of claim 3, it relates to the target equipment
Obtained by performing operational data, failure data, and inspection
Collect monitoring information such as test data offline
Since it was configured so that it does not complicate the system configuration.
The failure prediction information about the target equipment is
Due to the use of an expert system that can be provided to
There is an effect that a failure diagnosis device can be obtained.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of a failure diagnosis device using an expert system according to a first embodiment of the present invention.

FIG. 2 is an explanatory diagram showing a diagnostic prediction definition table for defining a diagnostic prediction condition for which diagnostic prediction is to be performed for each piece of monitoring information in the failure diagnostic device using the expert system according to the first embodiment of the present invention. is there.

FIG. 3 is an explanatory diagram showing a causal relationship table in which a relationship between a diagnosis / prediction condition and an estimated failure site in the failure diagnosis device using the expert system according to the first embodiment of the present invention is defined by a matrix structure.

FIG. 4 is an explanatory diagram showing a display screen showing a diagnosis prediction result displayed on the man-machine device in the failure diagnosis device using the expert system according to the first embodiment of the present invention.

FIG. 5 is a block diagram showing a configuration of a failure diagnosis device using an expert system according to a second embodiment of the present invention.

FIG. 6 is a block diagram showing a configuration of a conventional failure diagnosis device using an expert system.

[Explanation of symbols]

1 target equipment, 2 knowledge database, 3 monitoring information,
4 inference engine (failure factor determining means), 5 man-machine device (determination condition defining means), 6 diagnostic rule (determination rule, failure factor determining means), 7 prediction rule (determination rule, failure factor determining means), 8 online system .

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁷ Identification code FI G06F 11/22 360 G06F 11/22 360D (72) Inventor Mitsuo Abe 1-4, Mei Station, Nakamura-ku, Nagoya City Tokai Passenger Railroad Co., Ltd. (72) Inventor Genji Torii 1-4-1, Mei Station, Nakamura-ku, Nagoya City Tokai Passenger Railway Co., Ltd. (72) Inventor Shinichi Arimura 1-1-1, Wadazakicho, Hyogo-ku, Kobe No. 2 Mitsubishi Electric Co., Ltd. Control Works (72) Inventor Hidekazu Tanaka 1-2-2 Wadazaki-cho, Hyogo-ku, Kobe City Mitsubishi Electric Co., Ltd. Control Works (56) Reference JP-A-3-50700 (JP, A) JP-A-1-184599 (JP, A) JP-A 64-33697 (JP, A) JP-A-6-261378 (JP, A) JP-A-2-272326 (JP, A) JP-A-5 -60596 (JP, A) (58) Fields investigated (Int.Cl. ⁷ , DB name) G08B 23/00-31/00 G05B 13/00-13/04 G05B 23/00-23/02

Claims (3)

(57) [Claims] 1. A combination of monitoring information such as operation data and failure data regarding target equipment and monitoring information such as test data collected by performing inspection, failure diagnosis is performed based on a judgment rule of a knowledge database, and a failure is detected as a diagnosis result. In a failure diagnosis device that uses an expert system that identifies parts, based on the monitoring information obtained from the target equipment,
In addition, the judgment type and
The diagnostic rules that consist of
When the above phenomenon changes with it, the state of change of phenomenon, abnormality
Degree of status, probability of abnormal occurrence in the future, etc.
The judgment type to be applied to
Determination condition defining means for defining a prediction rule with a matrix structure, a determination type defined by the determination condition defining means, a determination condition and a determination condition when the failure occurs for each part of the target equipment based on the determination condition. A failure factor determining means for determining a failure factor by performing failure diagnosis and failure prediction before the failure occurs ;
In order to diagnose the monitoring information based on binary information
Acceptance judgment to be applied and conditions for making the judgment
And diagnostics based on threshold information that defines the upper and lower limits
Upper and lower limit judgment applied to
Applied to make a diagnosis based on conditions and fuzzy functions
Fuzzy decision and the conditions for making that fuzzy decision
It is applied to make a diagnosis based on
Stage judgment and the conditions and time for making the stage judgment
Suitable for making diagnosis based on the tendency that changes with the course of
The trend judgment to be used and the conditions for carrying out the trend judgment,
Difference between current phenomenon and past phenomenon
Difference judgment applied to make a diagnosis based on
That it has at least the conditions for making minute determinations
A fault diagnosis device that uses a characteristic expert system. [Claim 2] Operation data / fault data regarding the target equipment
Data such as monitoring data and test data obtained by performing inspections.
Characterized by collecting monitoring information online
A failure diagnosis device using the expert system according to claim 1 . [Claim 3] Operation data / fault data regarding the target equipment
Data such as test data obtained by
Characterized by collecting which monitoring information offline
A failure diagnosis device using the expert system according to claim 1 .