A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system. (February 2023)

• Record Type: Journal Article
• Authors: Pang, Zhenan; Li, Tianmei; Pei, Hong; Si, Xiaosheng
• Is Part Of: Reliability engineering & system safety. Volume 230(2023)
• Publication Date: 2023-02
• Subjects: remaining useful life -- unit-to-unit variability -- expectation-maximization (EM) -- extended Kalman filter (EKF); Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108854
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: An age- and state-dependent nonlinear degradation model is proposed for prognosis. Nonlinearity between measurements and hidden degradation is characterized by a state space model. Extended Kalman filtering and expectation-maximization algorithm are jointly applied to update the model. Analytical distribution of remaining useful life is derived and updated for online prognosis. Advantages in the prognosis accuracy are justified by numerical example and case study. Abstract: To deal with age- and state-dependent degradation, unit-to-unit variability and partially observable or hidden degradation in remaining useful life prediction jointly, this paper proposes a condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system. The dynamics and nonlinearity of the system degradation process are described by the age- and state-dependent nonlinear diffusion process. The nonlinear relationship between observations and the hidden degradation state is characterized by a state space model. To derive the distribution of the remaining useful life, we apply the extended Kalman filtering and expectation-maximization algorithm to adaptively estimate the degradation states and the unknown model parameters. Based on the estimated degradation states and model parameters, we derive the approximately analytical distribution of remaining useful life in the concept of the first hitting time. Furthermore, the distribution of remaining useful life can be updated according to the newly available data, thereby realizing real-time remaining useful life estimation. An illustrative example is given to explain the application of the proposed approach in the specific age- and state-dependent nonlinear degradation model. Finally, a numerical example and a case study for bearing degradation data are presented to verify the accuracy and effectiveness of the proposed model.