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Acta Armamentarii ›› 2022, Vol. 43 ›› Issue (3): 712-719.doi: 10.12382/bgxb.2021.0018

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Remaining Useful Life Prediction of Equipment under Random Degradation Stress

XU Xiaodong, TANG Shengjin, XIE Jian, YU Chuanqiang, WANG Fengfei, HAN Yangyang   

  1. (National Key Discipline Laboratory of Armament Launch Theory & Technology, Rocket Force University of Engineering, Xi'an 710025, Shaanxi, China)
  • Online:2022-04-07

Abstract: The effective remaining useful life prediction plays a key role in improving the reliability and safety of equipment. The uncertain internal aging state and external working condition have great affect on the degradation rate and state-of-health of equipment. A novel method for predicting the remaining useful life of equipment under the random stress is proposed. The random degradation stress is introduced into the aging process of equipment,and a degradation model for equipment is established based on Wiener process. An off-line prior parameter estimation method based on expectation maximization algorithm and particle swarm optimization algorithm is proposed. The random parameter is updated online in Bayesian framework,and the probability distribution function of the remaining useful life prediction result is derived. The proposed method is verified by the experimental degradation data of lithium-ion batteries. The results show that the proposed method can be used to effectively improve the prediction accuracy of remaining useful life and reduce the uncertainty of prediction results in considering the influence of random stress on the degradation law of equipment.

Key words: remainingusefullifeprediction, randomdegradationstress, Wienerprocess, Bayesianframework, expectationmaximizationalgorithm, particleswarmoptimizationalgorithm

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