A multi-time scale approach to remaining useful life prediction in rolling bearing. (15th January 2017)

• Record Type: Journal Article
• Authors: Qian, Yuning; Yan, Ruqiang; Gao, Robert X.
• Is Part Of: Mechanical systems and signal processing. Volume 83(2017)
• Page Start: 549
• Page End: 567
• Publication Date: 2017-01-15
• Subjects: Multi-time scale modeling -- Enhanced phase space warping -- Modified Paris crack growth model -- RUL prediction -- Rolling bearing; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.06.031
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: 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 . Highlights: We investigate a multi-time scale modelling approach for RUL prediction of rolling bearings. Phase space warping (PSW) technique is used to describe a dynamic system in a fast-time scale. Paris crack growth model is a used to characterize the dynamic system in a slow-time scale. The multi-time scale modeling approach can well track evolution of the bearing failure and accurately predict its RUL.