Wavelet Packet Envelope Manifold for Fault Diagnosis of Rolling Element Bearings

The vibration/acoustic signals of rolling element bearings contain rich information of health conditions for bearing maintenance. However, due to the complexity of measured signals, the status information is hard to be well extracted. This paper explores the envelope analysis technique for fault diagnosis of rolling element bearings, and proposes a new method, called wavelet packet envelope manifold (WPEM) approach, to extract the intrinsic envelope structure for well identification of the specific characteristic frequency. The WPEM method performs a manifold learning algorithm on a high-dimensional subband envelope feature space without the optimal band selection. In particular, the proposed method can be implemented by the following procedures. First, the wavelet packet transform is carried out to decompose the vibration signal into a series of subband signals at particular time-frequency subspaces. Then, the envelopes of these signals are achieved by the Hilbert transform to construct a high-dimensional WPE matrix. Finally, the manifold learning is addressed on the WPE matrix to learn the new WPEM feature. The WPEM integrates the envelope information of different subband frequency contents in a nonlinear way and exhibits the merit of in-band noise suppression, which is superior to the traditional enveloping methods based on a selected frequency band. The effects of the parameters on the WPEM performance are also discussed in this paper. The effectiveness is confirmed by practical applications to bearing fault diagnosis.