Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data

Reliability and remaining useful life (RUL) estimation for a satellite rechargeable lithium battery (RLB) are significant for prognostic and health management (PHM). A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data, including bivariate degradation data and lifetime data. Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system. First, linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB’s temperature and discharge voltage. Next, the Bayesian method, in combination with Markov Chain Monte Carlo (MCMC) simulations, is provided to integrate limited bivariate degradation data with other congeneric RLBs’ lifetime data. Then reliability evaluation and RUL prediction are carried out for PHM. A simulation study demonstrates that due to the data fusion, parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes. Finally, a practical case study of a satellite RLB verifies the usability of the model.摘要卫星蓄电池剩余剩余寿命预测是卫星系统故障诊断与健康管理的重要一环。 本文创新性的提出了一个融合二元性能退化数据和寿命数据的可靠性分析贝叶斯模型, 对卫星锂电池的剩余寿命进行预测。 二元性能退化是指系统存在两个相关的性能退化现象, 二者的共同作用下可能导致系统故障。 模型中首先利用 Copula 函数和线性维纳过程对锂电池的二元性能退化数据进行建模。 而后, 利用贝叶斯模型融合小子样寿命数据, 采用马尔科夫蒙特卡洛仿真估计模型参数, 从而对剩余寿命进行预测, 并通过仿真和实例分析对所提模型的性能进行分析。 结果表明融合了二元性能退化数据和寿命数据后, 剩余寿命的预测精度能够有效提高。