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An enhanced multipoint optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox

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Abstract

Previous research has shown that minimum entropy deconvolution (MED) is an effective technique for detecting impulse-like signals, such as the bearing fault and gear fault signals. However, some problems still exist in this technique. With the aim of overcoming these limitations, in this paper, an enhanced MED called multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. MOMEDA can succeed in detecting multiple impulses. Unfortunately, according to some simulations and real tests in this work, the results of applying this technique to the fault signals directly were grudgingly acceptable but not very satisfactory, especially under a harsh working condition. This means that MOMEDA is a little sensitive to intensive background noise and vibration interference. To overcome this drawback, a novel mode decomposition method, named time-varying filtering for empirical mode decomposition (TVFEMD), is applied to adaptively eliminate background noise and vibration interference prior to using MOMEDA. According to this proposed method, the weak bearing fault features can be identified clearly. The proposed approach is utilized in bearing fault detection of a spur gearbox and the results show its superiority and effectiveness.

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Acknowledgments

The authors thank Prof. Bob Randall for providing the bearing fault data freely in his published book. This work is supported by the Fundamental Research Funds for the Central Universities, CHD (No. 300102258714 and 30010223801), the National Natural Science Foundation of China (No. 51705030), and the Special Funds for Education and Teaching reform for the Central Universities (No. 310625176501).

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Authors and Affiliations

  1. School of Mechanical Engineering, Chang’an University, Shaanxi, 710064, China

    Yuanbo Xu, Zongyan Cai & Kai Ding

  2. School of Economics and Management, Chang’an University, Shaanxi, 710064, China

    Xiaoyan Cai

Authors
  1. Yuanbo Xu
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  2. Zongyan Cai
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  3. Xiaoyan Cai
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  4. Kai Ding
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Corresponding author

Correspondence to Zongyan Cai.

Additional information

Recommended by Associate Editor Kyoung-Su Park

Yuanbo Xu received a B.S. in Mechanical Engineering from Zhejiang SCI-TECH University, Hangzhou, China in 2009. He is currently a Ph.D. candidate at Chang’ an University. His primary research interest is machine fault diagnosis.

Zongyan Cai is a Professor of Mechanical Engineering at Chang’ an University. He received his Ph.D. from Northwestern Polytechnical University in China. His research interests include Intelligent robot technology, signal processing and Intelligent fault diagnosis.

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Xu, Y., Cai, Z., Cai, X. et al. An enhanced multipoint optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox. J Mech Sci Technol 33, 2573–2586 (2019). https://doi.org/10.1007/s12206-019-0505-9

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  • DOI: https://doi.org/10.1007/s12206-019-0505-9

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