《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 diagnosis method of planetary gear based on EEMD and GA-LSTM algorithms

Published:2023-11-27 author:TAO Haoran, XU Xin, PAN Hongxia, et al. Browse: 270 Check PDF documents

Fault diagnosis method of planetary gear based on EEMD and

GA-LSTM algorithms

TAO Haoran1, XU Xin2, PAN Hongxia1,2, WANG Tong1, XU Hongzhao1

(1.School of Mechanical Engineering, North University of China, Taiyuan 030051, China;

2.Institute of System Identification and Diagnostic Technology, North University of China, Taiyuan 030051, China)

Abstract: How to effectively extract weak fault features of planetary gears under strong background noise is a difficult problem that needs to be solved in the field of planetary gear fault diagnosis. For the nonlinear and non-stationary vibration signals of planetary gears, in order to improve the accuracy of fault diagnosis, a planetary gear fault diagnosis method optimized by genetic algorithm-optimized long-short-term memory network (GA-LSTM) and ensemble empirical mode decomposition (EEMD) was proposed. First, the vibration signals of four types of planetary gear faults were collected in the experiment, and the original vibration signal of the planetary gear was decomposed into six intrinsic mode function (IMF) components by using EEMD method, which was used as the feature components for further processing. Then, the hyperparameters of the LSTM network were optimized using the genetic algorithm (GA) to improve the accuracy of fault type identification. Finally, the feature components were inputted into the trained GA-LSTM model, the network model was used as the final classifier to diagnose and identify the faults of the planetary gears. By comparing the unoptimized network and artificially adding noise to the original signal to simulate the actual engineering signal, the validity and effectiveness of the method based on EEMD and GA-LSTM algorithms were verified effectiveness. The research results show that the trained network achieves a loss rate of less than 2%, and has good stability. The fault classification accuracy of the GA-LSTM method reaches 94.17%. Comparing with the non-optimized network, the verification accuracy of the GA-LSTM model is found to be higher than that of the LTSM, which shows better timing performance on all components; even when identifying engineering signals with added noise, high fault diagnosis accuracy can also be maintained, which shows its superiority in planetary gear fault diagnosis. This study has certain theoretical guidance and reference value in improving the fault diagnosis ability of mechanical transmission equipment.

Key words: gear transmission; strong background noise; weak fault characteristics; ensemble empirical mode decomposition (EEMD); long short-term memory (LSTM) network; classification accuracy; feature extraction; genetic algorithm (GA)

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