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
Abstract:
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.