《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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Fault feature extraction for RV reducer based on MCSA and Fisher-SAE

Published:2022-09-22 author:ZHANG Zi-qin, WANG Gui-yong, LIU Tao. Browse: 582 Check PDF documents

Fault feature extraction for RV reducer based on MCSA and FisherSAE

ZHANG Zi-qin1, WANG Gui-yong2, LIU Tao1

(1.Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology,

Kunming 650500, China; 2.Inner Mongolia First Machinery Group Co., Ltd., Baotou 014000, China)

Abstract: Aiming at the problems of installation space and signal acquisition of vibration sensors in the actual monitoring of RV reducers, which were prone to limitations and interference, a sparse autoencoder (SAE) and Fisher criterion combination method was proposed for RV gearbox fault feature extraction based on motor current signal analysis (MCSA). Firstly, the collected drive motor current data were converted to the frequency domain. The effect of different hyperparameters on the feature extraction ability of sparse autoencoder was investigated, and the sparse autoencoder with optimized parameters was used to automatically extract fault features from frequency domain signals. Then the Fisher criterion was used to rank the discriminative ability of the extracted features in descending order, and the top n features in the ranking were taken to obtain the optimal fault feature set. Finally, the SoftMax classification layer was combined to achieve fault diagnosis of RV reducers. The RV reducer fault test bench was built, the motor current data was collected, the method based on FisherSAE was verified, and it was compared with other typical machine learning fault diagnosis methods. The research results show that the method can extract fault features from the motor current signal of RV reducer, and select the most effective fault feature set, which solves the limitation of vibration signal and the problem that it is difficult to extract effective features by using current signal for fault diagnosis. Comparing with other typical machine learning fault diagnosis methods, the diagnostic accuracy of this method is increased by 10%~20%, and it has better diagnostic efficiency and accuracy.

Key words: gear reducer; fault diagnosis; fault feature extraction;motor current signal analysis (MCSA); sparse autoencoder (SAE); Fisher criterion; deep learning

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