Equipment fault diagnosis method based on improved negative selection algorithm of particle swarm algorithm

Abstract

The invention discloses an equipment fault diagnosis method based on an improved negative selection algorithm of a particle swarm algorithm, and the method comprises the steps of constructing a hash value character string self-set P1 formed by a, b and c strings and a to-be-detected string D1 through the frequency change trend of m points of an equipment current amplitude; constructing a hash value character string self-set P2 and a to-be-detected string D2 formed by 0 and 1 strings according to the relationship between the standard deviation of the m-point data and the standard deviation of all the data, generating a detector A and a detector B by a particle swarm algorithm, and calculating the distance between the to-be-detected string D1 and each sub-string of the detector A and the distance between the to-be-detected string D2 and each sub-string of the detector B in a Hamming distance mode; and obtaining an optimal value by generating each round of iteration of a particle swarm algorithm constructed by different substrings so as to solve detector overlapping and cross vulnerabilities. According to the method, the problem of loss caused by sudden failure of equipment in enterprise production can be well solved, and the problem that valuable equipment has no abnormal data and can be compared and analyzed can be well solved, so that the production can be better served.

Description

technical field [0001] The invention relates to the technical field of electrical equipment fault diagnosis under a non-invasive monitoring system, in particular to an equipment fault diagnosis method based on a negative selection algorithm improved by a 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 safety hazards . Therefore, producers and operators will think about how to obtain data about equipment failure in advance, give early warning, and achieve the purpose of reducing losses. ; [0003] At present, equipment fault diagnosis and early warning are generally based on artificial intelligence algorithms; or machine learning algorithms, or deep learning algorithms. It ...

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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 al

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