Application of Lyapunov exponent in mechanical vibration identification of asynchronous motor

被引：0
作者：
Liu Y. [1 ]
Gao K. [1 ]
Huang Y. [1 ]
Zhang H. [1 ]
Xiao J. [2 ]
机构：
[1] School of Mechatronic Engineering, Northwestern Polytechnical University, Xi'an
[2] Hefei General Machinery Research Institute Co.,Ltd., State Key Lab of Compressor Technology, Hefei
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 13期
关键词：
asynchronous motor; Brown Bryant Abarbanel (BBA) algorithm; fault vibration; Lyapunov exponent;
D O I：
10.13465/j.cnki.jvs.2022.13.019
中图分类号：
学科分类号：
摘要：
Here,based on the theory of nonlinear dynamics,Lyapunov exponent characteristics of an asynchronous motor vibration signals were studied and applied in fault diagnosis and identification. Firstly, a test platform was built to simulate 3 rotating states of the asynchronous motor including normal operation, rotor misalignment and poor base installation. Waveforms of the motor's 3 kinds of vibration signals were analyzed, denoised and preprocessed. Then, Lyapunov exponent spectra of vibration signals under different working states were calculated using BBA algorithm, and the maximum Lyapunov exponent was selected as the feature to identify mechanical vibration of the motor. Finally, to verify the effectiveness and anti-interference of the analysis method, random noise was introduced to analyze influence levels of noise on BBA algorithm under different parameters. The study results showed that the maximum Lyapunov exponent of the motor is in the range of 0. 3-0.7 during normal operation, the maximum Lyapunov exponent is in the range of 0-0. 3 when the motor is not installed properly, so the motor vibration signal sequences under these two working conditions come from a chaotic process ; when the motor is in the state of rotor misalignment,its maximum Lyapunov exponent is approximately zero, so there is basically no chaotic property in its vibration sequences; based on the study results,combined with feature fusion and machine learning classification algorithm, the accuracy and efficiency of mechanical vibration recognition of asynchronous motor can be effectively improved. © 2022 Chinese Vibration Engineering Society. All rights reserved.
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页码：142 / 151
页数：9
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