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| Early degradation detection of rolling bearing based on adaptive variational mode decomposition and envelope harmonic to noise ratio |
| L Mingzhu1,2, LIU Shixun3, SU Xiaoming1, CHEN Changzheng1 |
1. School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China;
2. College of Automatic Control, Liaoning Vocational Technology College of Equipment Manufacturing, Shenyang 110161, China;
3. North Lab, Co, Ltd, CQC (Shenyang), Shenyang 110164, China |
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Abstract Here, aiming at problems of weak fault features of rolling bearing being easy to be disturbed by noise and its degradation starting point being difficult to determine, an early degradation detection method of rolling bearing based on adaptive variational mode decomposition (AVMD) and envelope harmonic-to-noise ratio(EHNR) was proposed. Firstly, the grey wolf optimization algorithm was used to adaptively select the number of decomposition layers and quadratic penalty factor of AVMD, and the minimum average envelope entropy was taken as the objective function to obtain the optimal parametric combination. Secondly, the effective weighted sparse kurtosis index was introduced to separate effective modal components and noise modal components, and the reconstructed signal could filter out interference and retain fault information. Finally, the envelope harmonic to noise ratio of the reconstructed signal was calculated, and its sensitivity to periodic fault impact was used to detect the early degradation starting point of rolling bearing. The experimental verification results showed that the proposed method can not only solve the problem of false alarm in the early stage of bearing operation, but also earlier identify the starting point of bearing degradation process; it has both robustness and sensitivity, and provides a reference for early fault diagnosis and residual life prediction of rolling bearing.
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Received: 03 April 2020
Published: 15 July 2021
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