Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy

Fengyun Xie; Enguang Sun; Shengtong Zhou; Jiandong Shang; Yang Wang; Qiuyang Fan

doi:10.3390/e25101446

Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy

Entropy (Basel). 2023 Oct 13;25(10):1446. doi: 10.3390/e25101446.

Authors

Fengyun Xie^{1

2

3}, Enguang Sun¹, Shengtong Zhou^{1

2

3}, Jiandong Shang¹, Yang Wang¹, Qiuyang Fan¹

Affiliations

¹ School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
² State Key Laboratory of Performance Monitoring Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China.
³ Life-Cycle Technology Innovation Center of Intelligent Transportation Equipment, Nanchang 330013, China.

Abstract

Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests.

Keywords: Weibull distribution; fault diagnosis; multiscale dispersion entropy; particle swarm optimization; support vector machine.

Grants and funding

This research was funded by the National Natural Science Foundation of China (52265068, 52065022), the Natural Science Foundation of Jiangxi Province (20224BAB204050), the Carrier and Equipment Key Laboratory Project of the Ministry of Education (KLCEZ2022-02), and the Project of Jiangxi Provincial Department of Education (GJJ2200627).