Journal of Mechanical Science and Technology

, Volume 33, Issue 6, pp 2573–2586 | Cite as

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

  • Yuanbo Xu
  • Zongyan CaiEmail author
  • Xiaoyan Cai
  • Kai Ding


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.


Bearing fault detection Empirical mode decomposition Time varying filter Multipoint optimal minimum Entropy deconvolution 


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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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Copyright information

© KSME & Springer 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringChang’an UniversityShaanxiChina
  2. 2.School of Economics and ManagementChang’an UniversityShaanxiChina

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