Effects of Parameter Selection on Oscillatory Behavior-Based Signal Decomposition for Bearing Fault Signature Extraction

Oscillatory Behavior-based Signal Decomposition (OBSD) is a new technique which employs Morphological Component Analysis (MCA) and the Tunable Q-factor Wavelet Transform (TQWT) to decompose a signal into components consisting of different oscillatory behaviors rather than different frequency bands or scales. Due to the low oscillatory transients of bearing fault-induced signals, this method shows promise for application to effectively extract bearing fault signatures from raw signals contaminated by interferences and noise. In this paper, the application of OBSD to bearing fault signature extraction is investigated. It is shown that the quality of the results obtained via the OBSD is highly dependent on the selection of method-related parameters. The effects of each parameter on the performance of the OBSD for bearing fault signature extraction are investigated. The analysis is also validated by implementing the OBSD on experimental data collected from a test rig with a defective bearing.