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Equipment status diagnosis method and device

A technology of equipment status and diagnostic methods, which is applied in the direction of measuring devices, instruments, characters and pattern recognition, etc., can solve problems such as difficulty in ensuring the accuracy of status judgment results, reduce the possibility of misjudgment, achieve accurate results, and make judgments credible high degree of effect

Active Publication Date: 2017-10-10
ZHEJIANG SUPCON TECH
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  • Description
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AI Technical Summary

Problems solved by technology

With the diversification of monitored states and parameters and the complexity of the production process of the industry or other non-uniform objective conditions, the traditional fixed value monitoring technology is increasingly unable to adapt to this change, and it is difficult to guarantee the accuracy of the state judgment results. Accuracy
[0003] In order to solve this problem, under the existing technical conditions, experienced personnel are used to cooperate with the monitoring system to realize manual intervention and pre-judgment to make effective judgments. Due to differences in personal experience and various thinking inertias, this machine Combined manual methods have certain limitations

Method used

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  • Equipment status diagnosis method and device

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Experimental program
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Embodiment 1

[0048] see figure 1 It is a schematic flowchart of a method for diagnosing equipment status provided by Embodiment 1 of the present invention, the method includes the following steps:

[0049] S11. Process the acquired historical samples to obtain typical sample fault classifications and their characteristic values, wherein the historical samples are equipment historical fault sample data;

[0050]Specifically, after obtaining the historical sample data in the historical sample database, statistical methods are used to initially classify and organize the sample data, and the K-nearest neighbor algorithm is usually used for induction to obtain typical sample fault classifications. The number of classifications is determined by The combination of manual judgment and historical data collation will gradually increase. For example, if the historical fault sample data of a steam turbine is obtained, the fault classification of typical samples can be divided into sudden unbalance of...

Embodiment 2

[0061] Referring to embodiment one of the present invention and figure 1 The specific process of steps S11 to S16 described in , and see figure 2 , the step S12 performs a similarity judgment on the tested sample according to the typical sample fault classification and its characteristic value to obtain a preliminary fault classification of the tested sample, which specifically includes the following steps:

[0062] S121. Determine a judgment condition for each fault classification according to the typical sample fault classification and its characteristic value;

[0063] S122. According to the judgment condition, perform parameter correlation analysis on the tested sample, and judge to obtain a preliminary fault classification of the tested sample.

[0064] Specifically, after the fault classification of typical samples is obtained, clear judgment conditions are summarized for different classification situations, and then parameter correlation analysis and classification ar...

Embodiment 3

[0079] Corresponding to the equipment diagnosis method disclosed in the first and second embodiments of the present invention, the third embodiment of the present invention also provides an equipment diagnosis device, see Figure 4 , the device consists of:

[0080] The processing module 1 is used to process the acquired historical samples to obtain typical sample fault classifications and their characteristic values, wherein the historical samples are equipment historical fault sample data;

[0081] The first judging module 2 is used to judge the similarity of the tested sample according to the typical sample fault classification and its characteristic value to obtain a preliminary fault classification of the tested sample;

[0082] A classification module 3, configured to obtain a sample feature value corresponding to the preliminary classification of the fault, perform weighted calculation on the sample feature value to obtain a similarity parameter, and determine the fault...

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Abstract

The invention discloses an equipment status diagnosis method and an equipment status diagnosis device. The method comprises the following steps: processing an acquired historical sample to obtain a typical sample fault classification and feature values thereof; judging the similarity of to-be-detected samples to obtain a primary fault classification; acquiring the sample feature values corresponding to the primary fault classification, performing weighed calculation to obtain similarity parameters, and determining the fault classification of the to-be-detected samples; when the fault classification is single, determining the fault classification as a fault diagnosis classification of the to-be-detected samples; if not, establishing a precise model, and inputting the primary fault classification into the precise model for judgment to obtain the fault diagnosis classification of the to-be-detected samples. Through the equipment status diagnosis method and device, the purposes of improving the accuracy and precision of diagnosing the equipment status can be realized.

Description

technical field [0001] The invention relates to the technical field of equipment production monitoring, in particular to a method and device for intelligent diagnosis of equipment status based on machine learning. Background technique [0002] In the field of industrial production, the traditional equipment status monitoring scheme is judged by fixed threshold monitoring or simple multi-threshold monitoring, but this scheme can generally solve a class of status detection problems. With the diversification of monitored states and parameters and the complexity of the production process of the industry or other non-uniform objective conditions, the traditional fixed value monitoring technology is increasingly unable to adapt to this change, and it is difficult to guarantee the accuracy of the state judgment results. Accuracy. [0003] In order to solve this problem, under the existing technical conditions, experienced personnel are used to cooperate with the monitoring system ...

Claims

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Application Information

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IPC IPC(8): G01M99/00G06K9/62
CPCG01M99/00G06F18/22G06F18/241
Inventor 姚杰孔伟阳阮志坚马楠桦
Owner ZHEJIANG SUPCON TECH
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