A multi-time scale approach to remaining useful life prediction in rolling bearing

作者：Qian, Yuning; Yan, Ruqiang^*; Gao, Robert X.

来源：Mechanical Systems and Signal Processing, 2017, 83: 549-567.

DOI：10.1016/j.ymssp.2016.06.031

• Multi-time scale modeling
• Enhanced phase space warping
• Modified Paris crack growth model
• RUL prediction
• Rolling bearing

This paper presents a novel multi-time scale approach to bearing defect tracking and remaining useful life (RUL) prediction, which integrates enhanced phase space warping (PSW) with a modified Paris crack growth model. As a data-driven method, PSW describes the dynamical behavior of the bearing being tested on a fast-time scale, whereas the Paris crack growth model, as a physics-based model, characterizes the bearing's defect propagation on a slow-time scale. Theoretically, PSW constructs a tracking metric by evaluating the phase space trajectory warping of the bearing vibration data, and establishes a correlation between measurement on a fast-time scale and defect growth variables on a slow-time scale. Furthermore, PSW is enhanced by a multi-dimensional auto-regression (AR) model for improved accuracy in defect tracking. Also, the Paris crack growth model is modified by a time-piecewise algorithm for real-time RUL prediction. Case studies performed on two run-to-failure experiments indicate that the developed technique is effective in tracking the evolution of bearing defects and accurately predict the bearing RUL, thus contributing to the literature of bearing prognosis.

• 出版日期2017-1-15
• 单位东南大学