随机退化应力作用下设备剩余寿命预测方法

有效的剩余寿命预测对设备可靠性与安全性起着关键作用，不确定的内部老化状态和外部使用工况导致的随机退化应力会极大地影响设备的退化速率和健康状况。提出一种新的随机退化应力作用下的设备剩余寿命预测方法。将随机退化应力引入老化过程，基于维纳过程建立随机应力作用下的设备老化模型，提出融合期望最大化算法和粒子群优化算法的先验参数离线估计方法；在贝叶斯框架下在线更新随机系数，推导剩余寿命预测结果的概率密度分布函数。基于锂电池实验退化数据验证了所提方法的有效性。结果表明，考虑随机应力对设备退化规律的影响，能够有效提高剩余寿命的预测精度并降低预测结果的不确定性。

Remaining Useful Life Prediction of Equipment under Random Degradation Stress

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.