Fault Diagnosis Algorithm of Permanent Magnet Synchronous Motor Based on Improved ELM

Authors

Wang Xin, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: In order to address the common problems of lacking of phase and interturn short circuit fault, after analyzing the basic and corresponding fault model of permanent magnet synchronous motor(PMSM), an improved extreme learning machine (IELM) algorithm was proposed based on self-adaptive second-order particle swarm optimization (SaSECPSO). SaSECPSO employed adaptive inertia weight and cognitive coefficient with linear variation to improve the convergence rate and accuracy of second-order particle swarm optimization (SECPSO). In addition, the recognition rate of extreme learning machine (ELM) when solving the fault model of PMSM could be significantly improved by using SaSECPSO to simultaneously optimize input weights and hidden layer threshold. The extensive experiment was carried out by taking motor speed and phase current as multi-source sample, and the results validate that IELM has a higher diagnostic accuracy than other algorithms.

Recommended Citation

Xin, Wang; Yan, Wang; and Ji, Zhicheng (2020) "Fault Diagnosis Algorithm of Permanent Magnet Synchronous Motor Based on Improved ELM," Journal of System Simulation: Vol. 29: Iss. 3, Article 24.
DOI: 10.16182/j.issn1004731x.joss.201703024
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss3/24

First Page

646

Revised Date

2016-09-09

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201703024

Last Page

653

CLC

TP391.9

Recommended Citation

Wang Xin, Wang Yan, Ji Zhicheng. Fault Diagnosis Algorithm of Permanent Magnet Synchronous Motor Based on Improved ELM[J]. Journal of System Simulation, 2017, 29(3): 646-653.

DOI

10.16182/j.issn1004731x.joss.201703024