Affiliations: [a] Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing, China
| [b] School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, China
Correspondence:
[*]
Corresponding author. Yanxue Wang, Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, P.R. China. E-mail: yan.xue.wang@gmail.com.
Abstract: Roller bearings are among the most frequently encountered components in the majority of rotating machines. Thus, prognostic and health management of roller bearing plays an important role on the working conditions of the machine. Remaining useful life prediction is one of keys to apply PHM for practical applications. The collected bearing vibration signals are generally non-linear and non-stationary. However, those auto-regression model based methods are only suitable for the prediction of linear and stationary time series. Moreover, most of the existing machine learning based techniques require considerable training and parameter tunings which are time consuming and difficult for practical applications. To overcome these issues, a novel remaining useful life prediction method for rolling bearing prognostics is proposed in this work based on the sparse coding and sparse linear auto-regression model without training and parameter tunings. Sparse coding is formulated as a basis pursuit L1-norm problem, where a sparse set of weight can be estimated for each test vector. Sparse local linear and neighbor embedding are employed to construct the proposed weight constraint sparse coding method. Two different experimental validations are conducted to well demonstrate the effectiveness and robustness of the proposed method for remaining useful life prediction of bearing via root-mean-square, peak-to-peak and kurtosis indicators in time-domain.
Keywords: Prognostic and health management, trend prediction, remain useful life, sparse coding, roller bearing
