Transient-extracting wavelet transform for impulsive-like signals and application to bearing fault detection

Abstract

Being able to characterize impulsive-like signals and extract their transitory features is difficult due to the presence of noise and irrelevant signal components in real signals. To address these problems, a brand-new time-frequency (TF) analysis technique called the transient-extracting wavelet transform is developed. This method is put forth by first investigating which TF coefficients can represent the fundamental TF properties of impulsive signals, and then designing an extraction operator to get the most related TF coefficients while simultaneously removing the unrelated ones. The signal reconstruction of this method is also analyzed. Additionally, a transient feature extraction approach is suggested for pinpointing the impulse’s occurrence timing, which is essential for correctly identifying the fault type. The analysis shows that the suggested method is more able to analyze impulsive-like data and is an effective bearing defect detector.