Rolling element bearing fault feature extraction using EMD-based independent component analysis

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 self adaptively 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.