title: Degradation prediction model based on a neural network with dynamic windows.
abstract: Tracking degradation of mechanical components is very critical for effective maintenance decision making. Remaining useful life (RUL) estimation is a widely used form of degradation prediction. RUL prediction methods when enough run-to-failure condition monitoring data can be used have been fully researched, but for some high reliability components, it is very difficult to collect run-to-failure condition monitoring data, i.e., from normal to failure. Only a certain number of condition indicators in certain period can be used to estimate RUL. In addition, some existing prediction methods have problems which block RUL estimation due to poor extrapolability. The predicted value converges to a certain constant or fluctuates in certain range. Moreover, the fluctuant condition features also have bad effects on prediction. In order to solve these dilemmas, this paper proposes a RUL prediction model based on neural network with dynamic windows. This model mainly consists of three steps: window size determination by increasing rate, change point detection and rolling prediction. The proposed method has two dominant strengths. One is that the proposed approach does not need to assume the degradation trajectory is subject to a certain distribution. The other is it can adapt to variation of degradation indicators which greatly benefits RUL prediction. Finally, the performance of the proposed RUL prediction model is validated by real field data and simulation data.
authors: Mechanical Engineering College, Shijiazhuang 050003, China. dynamicbnt@aim.com.
authors: Xinghui
authors: X
authors: Zhang
authors: The State Key Lab of Mechanical Transmission, Chongqing University, Chongqing 400030, China. lx71@scarletmail.rutgers.edu.|Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA. lx71@scarletmail.rutgers.edu.
authors: Lei
authors: L
authors: Xiao
authors: Mechanical Engineering College, Shijiazhuang 050003, China. jskang201206@aim.com.
authors: Jianshe
authors: J
authors: Kang