An Age-Dependent and State-Dependent Adaptive Prognostic Approach for Hidden Nonlinear Degrading System

Remaining useful life (RUL) estimation approaches on the basis of the degradation data have been greatly developed, and significant advances have been witnessed. Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL. Most current researches focus on age-dependent degradation models, but it has been found that some degradation processes in engineering are also related to the degradation states themselves. In addition, due to different working conditions and complex environments in engineering, the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL. In order to solve the above issues jointly, we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states. Then, the Kalman filter (ICF) is utilized to update the hidden degradation states in real time, and the expectation-maximization (EM) algorithm is applied to adaptively estimate the unknown model parameters. Besides, the approximate analytical RUL distribution can be obtained from the concept of the first hitting time. Once the new observation is available, the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters. The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.

Automated summary

Significant advances have been made in the estimation of remaining useful life (RUL) based on degradation data. The establishment of an applicable degradation model is crucial for accurate RUL estimation, but current research mainly focuses on age-dependent degradation models. It has been found that degradation processes in engineering can also be related to degradation states. Additionally, unit-to-unit variability and unobservable degradation states due to different working conditions and complex environments affect the accuracy of RUL estimation. To address these issues, an age-dependent and state-dependent nonlinear degradation model is developed taking into consideration unit-to-unit variability and hidden degradation states. The Kalman filter is used to update the hidden degradation states in real time, while the expectation-maximization algorithm is applied to adaptively estimate unknown model parameters. The proposed approach is validated through numerical simulations and case studies on Li-ion batteries and rolling element bearings.