SVM classification model-based equipment fault diagnosing method

Abstract

The invention discloses an SVM classification model-based equipment fault diagnosing method. The method comprises the following steps: performing a preprocessing operation on equipment data; constructing a fault diagnosis case knowledge base; performing fault diagnosis on a support vector machine on the basis of an SVM classification model; acquiring fault information and performing maintenance guide. According to the support vector machine-based intelligent equipment fault diagnosis method, the fault features of equipment are highlighted to the maximum degree; the situations that the equipment data are incomplete and inaccurate are reduced; the possibility is provided for constructing an accurate and reliable fault diagnosis model; the problem that the diagnosis model ages along with the operating time of the equipment is solved; the misdiagnosis rate of the fault diagnosis is reduced; the accuracy and the speed of the equipment fault diagnosis are greatly improved.

Description

technical field [0001] The present invention mainly relates to the technical field of equipment fault diagnosis, and more specifically relates to a method in the research field of equipment fault diagnosis based on SVM classification. Background technique [0002] In some important occasions such as power plants, ironworks, satellite launch sites, etc., in order to ensure the safe operation of key equipment, factories often invest a large number of maintenance personnel to ensure the safe operation of the equipment. However, due to their own diagnostic skills or attention problems, the staff inevitably ignore some signs of abnormal equipment. Once the abnormal equipment develops into a production failure, it will bring huge economic losses to the enterprise. Therefore, researchers in related fields have invested a lot of energy to establish an intelligent equipment diagnosis system for production enterprises to ensure the safe operation of important equipment in the factory....

AI Technical Summary

Problems solved by technology

The present invention can highlight the fault characteristics of the equipment to the greatest extent through the regression filtering method, reduce the incomplete and inaccurate situation of the equipment data, and provide the possibility for building an accurate and reliable fault diagnosis model; the present invention is based on the use of support vector machines , use a simple and convenient way to realize the function of model incremental learning, to solve the problem of diagnostic model aging with equipment running time, and reduce the misdiagnosis rate of fault diagnosis model; the present invention uses support vector machines on the basis of identifying new faults , to realize the continuous improvement of the fault knowledge base; the fault diagnosis model realized by the present invention can provide more detailed fault diagnosis information, not only the simple fault type, but also includes the reliability, fault location and origin of each fault that this fault belongs to The fault maintenance guidance and suggestions extracted from the expert knowledge base greatly improved the accuracy and speed of equipment fault diagnosis

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