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Signals generated by transient vibrations in rolling bearings due to structural defects are non-stationary in nature, and reflect upon the operation condition of the bearing. Consequently, effective processing of non-stationary signals is critical to bearing health monitoring. This paper presents a comparative study of four representative time-frequency analysis techniques commonly employed for non-stationary signal processing. The analytical framework of the short-time Fourier transform, wavelet transform, wavelet packet transform, and Hilbert-Huang transform are first presented. The effectiveness of each technique in detecting transient features from a time-varying signal is then examined, using an analytically formulated test signal. Subsequently, the performance of each technique is experimentally evaluated, using realistic vibration signals measured from a bearing test system. The results demonstrate that selecting appropriate signal processing technique can significantly affect defect identification and consequently, improve the reliability of bearing health monitoring.
Gao et al. (Sun,) studied this question.