《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 method based on multi-signals improved empirical Fourier decomposition method

Published:2024-10-30 author:ZHU Danchen, HE Wei, ZHU Qunwei. Browse: 145 Check PDF documents

Fault feature extraction method based on multi-signals improved

empirical Fourier decomposition method

ZHU Danchen1, HE Wei2, ZHU Qunwei3

(1.Department of Electro-mechanics, Naval Petty Officer Academy, Bengbu 233012, China; 2.College of

Mechanical Engineering, Jiujiang Vocational and Technical College, Jiujiang 332007, China; 3.Guangzhou

Bureau, Naval Equipment Department, Guangzhou 510220, China)

Abstract: To address the problem that the bearings fault signals are usually contaminated by strong background interference due to multiple structures and complex transmission path, which affects accurate fault feature extraction of the bearings. A multi-signals improved empirical Fourier decomposition (MS-IEFD) method was proposed. Firstly, to fully utilize the fault characteristics in multiple signals,the improved empirical Fourier decomposition was applied to two signals acquired from different measuring points, the optimal decomposition number was determined based on the correlation coefficients threshold 0.1 between each modal functions and the original signal. Secondly, the weighted harmonics significant index was constructed to optimize the initial band division results, it reduced the invalid frequency bands with too narrow bandwidth and determined the optimal modal components. Then, with the advantage of cross-correlation analysis, the optimal modal functions of the two signals were analyzed and the fault signature was further enhanced. With the help of fast Fourier transform, the fault features of bearings were accurately extracted and the type of bearing fault was judged. Finally, the MS-IEFD method was applied to the simulation and experimental signals. Two simulation signals with the signal-to-noise ratios of -10 dB and -15 dB were constructed to simulate the signals at different measurement points, the bearing vibration signals at different measurement points were selected for the experimental analysis. The research results show that the MS-IEFD method can effectively extract the fault characteristics of rolling element bearing from strong background interference. When comparing with methods such as the variational mode decomposition (VMD), the effectiveness of the MS-IEFD method is further highlighted.

Key words: rolling element bearing; fault diagnosis; multi-signals; frequency band division; cross-correlation spectrum; multi-signals improved empirical Fourier decomposition (MS-IEFD); variational mode decomposition (VMD)

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