Equipment Fault Diagnosis Method Based on Improved Negative Selection Algorithm of Particle Swarm Optimization

Abstract

The invention discloses an equipment fault diagnosis method based on a negative selection algorithm improved by a particle swarm algorithm. The self-set P1 of a hash value string composed of a, b, and c strings and a waiting list are constructed through the frequency change trend of m points of the current amplitude of the equipment. Detect string D1, construct a self-set P2 of hash value strings composed of 0 and 1 strings and a string to be detected D2 through the relationship between the standard deviation of m-point data and the standard deviation of all data. Detector A and detector B use particle swarm algorithm At the same time, the distance between the string to be detected D1 and each substring of detector A and the distance between the string to be detected D2 and each substring of detector B are calculated by Hamming distance. Iteratively obtain optimal values ​​to address detector overlap and crossover vulnerabilities. The method of the invention can better solve the problem of loss due to sudden equipment failure in the production of the enterprise, and the problem that there is no abnormal data for valuable equipment that can be compared and analyzed, so as to better serve the production.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of electrical equipment under a non-invasive monitoring system, in particular to an equipment fault diagnosis method based on an improved negative selection algorithm of particle swarm algorithm. Background technique [0002] At present, various electrical appliances are used in the production and operation of enterprises, and electrical appliances may fail during operation, and once a failure occurs, it may cause huge economic losses to the production and operation of the enterprise, and more seriously, it may cause potential safety hazards. . As a result, manufacturers and operators will think about how to obtain data about the impending failure of equipment in advance, and give early warning to achieve the purpose of reducing losses. [0003] At present, equipment fault diagnosis and early warning are generally performed through artificial intelligence algorithms; or machine learning a...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings in the above-mentioned background technology, and provide a device fault diagnosis method based on the improved negative selection algorithm of the particle swarm algorithm, which can better solve the problem of loss caused by sudden failure of equipment in the production of enterprises, and expensive The equipment has no abnormal data that can be compared and analyzed, so as to better serve production

Images