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A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes

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  • Mengyao Gu

    (Chongqing University)

  • Youling Chen

    (Chongqing University)

Abstract

The similarity-based residual useful life prediction (SbRLP) approach, a burgeoning technique, plays an increasingly important role in the residual useful life (RUL) prediction. Currently, studies on taking (a) the selection of degradation indicatorsf; and (b) the selection of reference samples into account are rare. While they have positive influence on improving the performance of the SbRLP method with multiple degradation indicator (MSbRLP). Driven by the abovementioned gap, two novel selection processes are advanced by this article and thus an improved MSbRLP method is developed. Then, in case study of the gyroscope RUL estimation, two selection processes and the improved MSbRLP method are illustrated to be reasonable and effective by comparison with the classical MSbRLP method. Furthermore, implementing results reveal that the existence of degradation indicator having bad reflection effect will truly increase the recognition error of the degradation state and hence decrease the accuracy of RUL estimation.

Suggested Citation

  • Mengyao Gu & Youling Chen, 2018. "A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 987-998, October.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0708-y
    DOI: 10.1007/s13198-018-0708-y
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    References listed on IDEAS

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    1. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    2. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
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    Cited by:

    1. Nikhil M. Thoppil & V. Vasu & C. S. P. Rao, 2021. "Health indicator construction and remaining useful life estimation for mechanical systems using vibration signal prognostics," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 1001-1010, October.

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