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Volume 47 Issue 12
Dec.  2021
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Journal of Beijing University of Aeronautics and Astronautics  > 2021  >  47(12): 2542-2551.
QI Feng, WANG Mu-zhong, SHAO Ding-rong, et al. Optimal Period Interleaver for Turbo Codes[J]. Journal of Beijing University of Aeronautics and Astronautics, 2000, 26(4): 381-384. (in Chinese)
Citation: WAN Changhao, LIU Zhiguo, TANG Shengjin, et al. Remaining useful life prediction method based on random coefficient regression model with imperfect prior information[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(12): 2542-2551. doi: 10.13700/j.bh.1001-5965.2020.0439(in Chinese)
shu

Remaining useful life prediction method based on random coefficient regression model with imperfect prior information

doi: 10.13700/j.bh.1001-5965.2020.0439
  • 1.

    Missile engineering College, Rocket Force University of Engineering, Xi'an 710025, China

  • 2.

    Operational Support College, Rocket Force University of Engineering, Xi'an 710025, China

Funds:

National Natural Science Foundation of China 61703410

National Natural Science Foundation of China 61922089

National Natural Science Foundation of China 61573366

National Natural Science Foundation of China 61573076

National Natural Science Foundation of China 61773386

National Natural Science Foundation of China 61873273

National Natural Science Foundation of China 61873175

Basic Research Plan of Shaanxi Natural Science Foundation of China 2017JQ6015

Key Project at B Class of Beijing Natural Science Foundation of China KZ201710028028

More Information
  • Corresponding author: TANG Shengjin, E-mail: tangshengjin27@126.com
  • Received Date: 20 Aug 2020
  • Accepted Date: 09 Oct 2020
  • Publish Date: 20 Dec 2021
    Abstract

  • Remaining Useful Life (RUL) prediction is a core problem of equipment prognostics and health management. Accurate RUL prediction can make effective maintenance management before the failure occurs to reduce the probability of equipment failure. A heuristic RUL prediction method is proposed to overcome the problem of imperfect prior information or lack of prior information in actual RUL prediction. First, the nonlinear Random Coefficient Regression (RCR) model is used for degradation modelling. Then, the relationship of the parameter estimation results between the Expected Maximization (EM) algorithm and the Maximum Likelihood Estimation (MLE) method based on the field degradation data of single equipment is studied and the conclusion that the result of EM algorithm finally converges to that of MLE method is obtained. Based on this conclusion, a heuristic RUL prediction method is proposed, which fuses both prior information and field information. Finally, the proposed results and algorithm are estimated by the numerical simulation data and practical degradation data of lithium battery. The experimental and simulation results show that, compared to the traditional Bayesian method, the heuristic RUL prediction method can overcome the impact of imperfect prior information and has higher prediction accuracy.

     

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