Singularity analysis using continuous wavelet transform for bearing fault diagnosis

In this paper. wavelet transform is applied to detect abrupt changes in the vibration signals obtained from operating bearings being monitored. In particular, singularity analysis across all scales of the continuous wavelet transform is performed to identify the location (in time) of defect-induced bursts in the vibration signals. Through modifying the intensity of the wavelet transform modulus maxima, defect-related vibration signature is highlighted and can be easily associated with the bearing defect characteristic frequencies for diagnosis. Due to the fact that vibration characteristics of faulty bearings are complex and defect-related vibration signature is normally buried in the wideband noise and high frequency structural resonance. simple signal processing cannot be used to detect bearing fault. We show, through experimental results, that the proposed method has the ability to discriminate noise from the signal significantly and is robust to bearing operating conditions, such as load and speed. and severity, of the bearing damage. These properties are desirable for automatic detection of machine faults. (C) 2002 Published by Elsevier Science Ltd.