《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Bearing fault detection method of PMSM based on VMD and MLP

Published:2022-09-22 author:HUANG Xiao-cheng, HE Qing-chuan, CHEN Wen-hua Browse: 602 Check PDF documents

Bearing fault detection method of PMSM based on VMD and MLP

HUANG Xiao-cheng, HE Qing-chuan, CHEN Wen-hua

(National and Local Joint Engineering Research Center of Reliability Analysis and Testing for Mechanical and

Electrical Products,Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract: Aiming at the problem of the low detection accuracy of the existing permanent magnet synchronous motor(PMSM) bearing fault detection methods, the study of the representation method of the PMSM bearing fault and the detection method based on neural network were executed.A method for constructing a normalized characterization index set for PMSM bearing faults, and a PMSM bearing fault detection method based on VMD and MLP were proposed.Firstly, a set of PMSM bearing fault characterization indices was constructed by a method of fusing the bearing fault frequency domain characteristic parameters of PMSM and normalizing them. Then, the optimized variational mode decomposition(VMD) method was used to denoise and reconstruct the vibration signals,extract the bearing fault frequency domain characteristic parameters, and the normalized set of PMSM bearing fault characterization indices was calculated. The neural network model based on the multilayer perceptron(MLP) was used to train the normalized set of PMSM bearing fault characterization indices, and a model of PMSM bearing fault detection with high detection accuracy was obtained. Finally, a set of test and test device which can simulate the feed drive system of NC machine tool was adopted. The validity and advanced nature of the PMSM bearing fault detection method based on VMD and MLP were verified.The experimental results show that the proposed normalized set of PMSM bearing fault characterization indices has stronger fault characterization ability than the existing ones, and the average detection accuracy of the proposed method is up to 95.4%. The experimental results verify the advanced nature of the normalized set of PMSM bearing fault characterization indices, and the proposed bearing fault detection method of PMSM based on the VMD， and the effectiveness of PMSM bearing fault detection method based on VMD and MLP.

Key words: bearing fault feature extraction; permanent magnet synchronous motor (PMSM); fault characterization;neural network;variational mode decomposition (VMD); multi-layer perceptron (MLP);normalization processing

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