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

A particle swarm algorithm and negative selection technology, applied in the field of electrical equipment fault diagnosis, can solve problems such as equipment loss, achieve the effects of reducing operating costs, reducing time complexity, and expanding coverage

Active Publication Date: 2021-02-09
SICHUAN CHANGHONG ELECTRIC CO LTD
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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 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

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

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Embodiment 1

[0041]An equipment fault diagnosis method based on the negative selection algorithm improved by the particle swarm optimization algorithm, which specifically solves the defect of insufficient abnormal samples through the negative selection algorithm. The self-set P1 of hash value strings composed of , c strings and the string to be detected D1 are constructed through the relationship between the standard deviation of the m-point data and the standard deviation of all data to construct the self-set of hash value strings P2 and the strings to be detected. String D2, detectors A and B are generated by particle swarm optimization algorithm, and the distances between D1 and detector A substrings and the distances between D2 and detector B each substring are calculated by Hamming distance; Improvement, that is, the optimal value is obtained in each round of iteration of the particle swarm optimization algorithm constructed by generating different substrings, so as to solve the proble...

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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 ...

Claims

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Application Information

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IPC IPC(8): G06F16/903G06F16/906G06N3/00
CPCG06F16/90344G06F16/906G06N3/006
Inventor 何金辉宋佶聪王浩磊李哲
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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