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A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition

Author

Listed:
  • Lei Xiao

    (Chongqing University)

  • Xiaohui Chen

    (Chongqing University)

  • Xinghui Zhang

    (Mechanical Engineering College)

  • Min Liu

    (Chongqing University)

Abstract

Remaining useful life prediction methods are extensively researched based on failure or suspension histories. However, for some applications, failure or suspension histories are hard to obtain due to high reliability requirement or expensive experiment cost. In addition, some systems’ work condition cannot be simulated. According to current research, remaining useful life prediction without failure or suspension histories is challenging. To solve this problem, an individual-based inference method is developed using recorded condition monitoring data to date. Features extracted from condition data are divided by adaptive time windows. The time window size is adjusted according to increasing rate. Features in two adjacent selected windows are regarded as the inputs and outputs to train an artificial neural network. Multi-step ahead rolling prediction is employed, predicted features are post-processed and regarded as inputs in the next prediction iteration. Rolling prediction is stopped until a prediction value exceeds failure threshold. The proposed method is validated by simulation bearing data and PHM-2012 Competition data. Results demonstrate that the proposed method is a promising intelligent prognostics approach.

Suggested Citation

  • Lei Xiao & Xiaohui Chen & Xinghui Zhang & Min Liu, 2017. "A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1893-1914, December.
  • Handle: RePEc:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1077-x
    DOI: 10.1007/s10845-015-1077-x
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    References listed on IDEAS

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    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.
    3. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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    Cited by:

    1. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
    2. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
    3. Yixiao Zhao & Yihai He & Fengdi Liu & Xiao Han & Anqi Zhang & Di Zhou & Yao Li, 2020. "Operational risk modeling based on operational data fusion for multi-state manufacturing systems," Journal of Risk and Reliability, , vol. 234(2), pages 407-421, April.

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