A Method of Equipment Fault Diagnosis Based on SVM Classification Model

Abstract

A device fault diagnosis method based on the SVM classification model, including preprocessing operations for device data; constructing a fault diagnosis case knowledge base; based on the SVM classification model, performing fault diagnosis on support vector machines; The equipment fault intelligent diagnosis method of the support vector machine highlights the fault characteristics of the equipment to the greatest extent, reduces the situation of incomplete and inaccurate equipment data, provides the possibility to build an accurate and reliable fault diagnosis model, and solves the problem of aging of the diagnosis model with the operation time of the equipment. It reduces the misdiagnosis rate of the fault diagnosis model, and greatly improves the correct rate and speed of equipment fault diagnosis.

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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