Remaining Useful Life Prediction for Nonlinear Degraded Equipment With Bivariate Time Scales

• Main Authors: Hong Pei, Changhua Hu, Xiaosheng Si, Jianfei Zheng, Qi Zhang, Zhengxin Zhang, Zhenan Pang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Bivariate time scales; diffusion process; nonlinear degradation; Kalman filtering; remaining useful life (RUL) prediction
• Online Access: https://ieeexplore.ieee.org/document/8892551/

As the fundamental and prerequisite work of remaining useful life (RUL) prediction, degradation modeling directly affects the accuracy of RUL prediction. Existing degradation models are all developed under a single time scale, and less research has been carried out to consider the impact of multiple time scales on the degradation model. Toward this end, we mainly study a nonlinear degradation modeling and RUL prediction method for nonlinear stochastic degraded equipment with bivariate time scales in this paper. Firstly, a nonlinear degradation model considering the influence of two time scales is constructed based on the diffusion process. At the same time, the relationship between the two scales is quantitatively characterized by random proportional coefficient. Then, the analytical expressions of the life and RUL for nonlinear degraded equipment are derived under the concept of first passage time (FPT). In order to realize the adaptive estimation of parameters, the model parameters estimation method based on maximum likelihood estimation (MLE) and Kalman filtering algorithm is developed in this paper. Finally, numerical simulation and the monitoring data of gyroscope verify the effectiveness and superiority of the proposed method. The experimental results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction, and has a broad engineering application space.