Application of PSO Improved Algorithm in Motor Fault Diagnosis Simulation

Aiming at the shortcomings of the traditional fault diagnosis methods of electric motors, the author proposes a method based on the application of improved PSO algorithm in the simulation of motor fault diagnosis. The method optimizes the fault diagnosis method of BP neural network by adopting the a...

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Vydáno v:Wireless communications and mobile computing Ročník 2022; číslo 1
Hlavní autor: Shan, Yingjie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Hindawi 2022
John Wiley & Sons, Inc
Témata:
Algorithms
Back propagation networks
Bearings
Computer simulation
Electric motors
Fault diagnosis
Feature extraction
High temperature
Magnetic fields
Mutation
Optimization
Particle swarm optimization
Signal processing
Swarm intelligence
ISSN:1530-8669, 1530-8677
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Shrnutí:Aiming at the shortcomings of the traditional fault diagnosis methods of electric motors, the author proposes a method based on the application of improved PSO algorithm in the simulation of motor fault diagnosis. The method optimizes the fault diagnosis method of BP neural network by adopting the adaptive mutation particle swarm algorithm. Firstly, the fault features are extracted from the response signals of the measurable points of the motor to be tested, and wavelet packet decomposition and normalization are performed to construct a sample set; then, the particle swarm improvement algorithm is used to optimize the weights and thresholds of the BP neural network, so as to realize the training and testing of the motor to be tested. In the fault diagnosis of a certain motor, it is found that the fault diagnosis time and diagnosis rate of this method are significantly improved compared with the previous ones, and the diagnosis rate reaches 99% when the center deviation range is 0.3, the efficiency of the fault diagnosis model for parameter optimization through the improved PSO algorithm is higher, and the accuracy of the diagnosis results is further improved.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/2386523