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.

Reliability modeling of the bivariate deteriorating product with both monotonic and non-monotonic degradation paths

Fiber optical gyroscope(FOG)is a highly reliable navigation element,and the degradation trajectories of its two accuracy indexes are monotonic and non-monotonic respectively.In this paper,a flexible accelerated degradation testing(ADT)model is used for analyzing the bivariate dependent degradation process of FOG.The time-varying copulas are employed to consider the dynamic dependency structure between two marginal degradation processes as the Wiener process and the inverse Gaussian process.The statistical inference is implemented by utilizing an inference function for the margins(IFM)approach.It is demonstrated that the proposed method is powerful in modeling the joint distribution with various margins.

Bivariate Constant-Stress Accelerated Degradation Model and Inference Based on the Inverse Gaussian Process

Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliability of these products. This paper conducts a Bayesian analysis for a bivariate constant-stress accelerated degradation model based on the inverse Gaussian(IG) process. We assume that the product considered has two QCs and each of the QCs is governed by an IG process. The relationship between the QCs is described by a Frank copula function. We also assume that the stress on the products affects not only the parameters of the IG processes, but also the parameter of the Frank copula function. The Bayesian MCMC method is developed to calculate the maximum likelihood estimators(MLE) of the model parameters. The reliability function and the mean-time-to-failure(MTTF) are estimated through the calculation of the posterior samples. Finally, a simulation example is presented to illustrate the proposed bivariate constant-stress accelerated degradation model.

A finite-time maintenance policy of deteriorating systems with multiple dependent performance characteristics

Condition monitoring and health management of deteriorating systems have attracted significant attentions in reducing failure rate and ensuring normal operation of systems.However,related existing methodologies usually confront difficulty when applied to systems with multivariate performance characteristics(PCs)in the presence of model uncertainty.In this work,a periodic inspection and maintenance policy based on bivariate degradation process is proposed,where each PC is modeled by a gamma process with time-scale transformation.Further,copula functions are used to model the dependency between degradation increments.The uncertainty of model parameters is estimated by the Bayesian Markov chain Monte Carlo(MCMC)algorithm.Subsequently,the maintenance policy is optimized by minimizing the expected cost under parameter uncertainty.The expectation and variance of maintenance cost are computed over a finite-time horizon.Finally,a real example of heavy machine tools is applied to illustrate the proposed maintenance model.Numerical results show that the proposed maintenance policy provides a more robust and accurate solution in maintenance decision-making for deteriorating systems,leading to lower maintenance cost compared with other policies.