2011 IEEE Conference on Prognostics and Health Management, 20-23 June, 2011

Rolling Element Bearing Fault Feature Extraction Using EMD-Based Independent Component Analysis

Michael Pecht1 , C.H. Wu2, C.H. Yang2, S.C. Lo3, N. Vichare4, E. Rhem4
1CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
2Graduate Institute of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan
3Graduate Institute of Information and Logistics Management, National Taipei University of Technology, Taipei, Taiwan
4Dell Client Systems Reliability Engineering, Dell Computer Corp., Austin, TX, USA


This paper introduces a joint bearing fault characteristic frequency detection method using empirical mode decomposition (EMD) and independent component analysis (ICA). Independent component analysis can be used to separate multiple sets of one-dimensional time series into independent time series, which need at least two transducers to obtain more than one set of time series for separation of different sources. To overcome this restriction, preprocessing is needed to construct multiple sets of time series. Empirical mode decomposition has attracted attention in recent years due to its ability to selfadaptively process non-stationary and non-linear signals with multiple intrinsic mode functions being obtained through EMD decomposition. Hence, considering this superiority, this paper employs EMD to transform one set of one-dimensional series into multiple sets of one-dimensional series for pre-processing. After that, independent components (IC) are extracted, which include fault-related signatures in the frequency spectrum. To validate the proposed method, real motor bearing vibration data, including normal bearing data, outer race fault data, and inner race fault data, are used in a case study. The results show that the proposed method can be used for bearing fault extraction.

This article is available online here and to CALCE Consortium Members for personal review.

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