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An Adaptive Remaining Life Prediction for Rolling Element Bearings

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Abstract

In order to select the effective health index and build reasonably the prediction model for prognostics, a new approach is proposed. The generative topographic mapping-based negative likelihood probability is used as the health index, and K-means clustering algorithm is employed for state division. The adaptive prediction model based on Markov model and least mean square algorithm is built by the historical data and the online monitoring data. According to the given threshold, the remaining life can be captured. Based on experimental verification, the results indicate that the selected health index is able to effectively reflect the condition of rolling bearings and the proposed model shows high prediction accuracy in comparison to the common one.

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Acknowledgments

The work described in this paper was supported by a grant from the National Defence Researching Fund (No. 9140A27020413JB11076).

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Authors and Affiliations

  1. Naval University of Engineering Power Engineering Marine Engineering, Wuhan, 430033, China

    Shuai Zhang, Yongxiang Zhang & Jieping Zhu

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  1. Shuai Zhang
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  2. Yongxiang Zhang
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  3. Jieping Zhu
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Correspondence to Yongxiang Zhang.

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Zhang, S., Zhang, Y. & Zhu, J. An Adaptive Remaining Life Prediction for Rolling Element Bearings. J Fail. Anal. and Preven. 15, 82–89 (2015). https://doi.org/10.1007/s11668-014-9906-3

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  • DOI: https://doi.org/10.1007/s11668-014-9906-3

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