Remaining Useful Life Prediction for a Nonlinear Heterogeneous Wiener Process Model With an Adaptive Drift

• Published in: IEEE transactions on reliability 2015-06, Vol.64 (2), p.687-700
• Main Authors: Huang, Zeyi, Xu, Zhengguo, Wang, Wenhai, Sun, Youxian
• Format: Article
• Language: English
• Subjects: Adaptive drift; Bayes' theorem-based filter; Bayes' theorem-based smoother; Data models; Degradation; expectation-maximization algorithm; Mathematical model; Maximum likelihood estimation; nonlinear degradation trajectory; Prediction algorithms; Predictive models; remaining useful life prediction; Trajectory
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2015.2403433

Nonlinear degradation trajectories are encountered frequently, and not all of them evolve homogeneously in practical systems. To take nonlinearity, heterogeneity, and the entire historical degradation data into account, we propose a nonlinear heterogeneous Wiener process model with an adaptive drift to characterize degradation trajectories. A state-space based method is employed to delineate our model. Due to the introduction of the adaptive drift, it is difficult to directly apply Kalman filter methods to update the distribution of the estimated degradation drift. To address this issue, we develop an online filtering algorithm based on Bayes' theorem. The expectation-maximization (EM) algorithm, as well as a novel Bayes'-theorem-based smoother, are adopted to estimate the unknown parameters in our model. Moreover, the distribution of the predicted remaining useful life (RUL) incorporating the complete distribution of the estimated degradation drift is achieved analytically. Finally, a simulation, and a case study are provided to validate the proposed approach.