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Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system

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  • Junxun Chen
  • Longsheng Cheng
  • Hui Yu
  • Shaolin Hu

Abstract

For the timely identification of the potential faults of a rolling bearing and to observe its health condition intuitively and accurately, a novel fault diagnosis and health assessment model for a rolling bearing based on the ensemble empirical mode decomposition (EEMD) method and the adjustment Mahalanobis–Taguchi system (AMTS) method is proposed. The specific steps are as follows: First, the vibration signal of a rolling bearing is decomposed by EEMD, and the extracted features are used as the input vectors of AMTS. Then, the AMTS method, which is designed to overcome the shortcomings of the traditional Mahalanobis–Taguchi system and to extract the key features, is proposed for fault diagnosis. Finally, a type of HI concept is proposed according to the results of the fault diagnosis to accomplish the health assessment of a bearing in its life cycle. To validate the superiority of the developed method proposed approach, it is compared with other recent method and proposed methodology is successfully validated on a vibration data-set acquired from seeded defects and from an accelerated life test. The results show that this method represents the actual situation well and is able to accurately and effectively identify the fault type.

Suggested Citation

  • Junxun Chen & Longsheng Cheng & Hui Yu & Shaolin Hu, 2018. "Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 147-159, January.
  • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:1:p:147-159
    DOI: 10.1080/00207721.2017.1397804
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    References listed on IDEAS

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    1. A. Ngaopitakkul & S. Bunjongjit, 2013. "An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(9), pages 1745-1761.
    2. Jin, Guang & Matthews, David E. & Zhou, Zhongbao, 2013. "A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 7-20.
    3. Jia, Xiaodong & Jin, Chao & Buzza, Matt & Wang, Wei & Lee, Jay, 2016. "Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves," Renewable Energy, Elsevier, vol. 99(C), pages 1191-1201.
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    Cited by:

    1. Ning Wang & Zhuo Zhang & Jiao Zhao & Dawei Hu, 2022. "Recognition method of equipment state with the FLDA based Mahalanobis–Taguchi system," Annals of Operations Research, Springer, vol. 311(1), pages 417-435, April.

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