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Abstract. To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA) using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD) and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.

The Synchrosqueezing is a special case of the reassignment method and concentrates the timefrequency representation (TFR) in a scale dimension. Compared to other TFR enhancement methods, the synchrosqueezing offers better adaptability, less deformation for IF profile and an exact reconstruction formula for constituent components. This paper deals with the investigation of descriptors based on the combination of the synchrosqueezing transform (SST) and Lempel-Ziv complexity methods. This last one transforms the analyzed signal into a data sequence. In the first part, the vibration signal components are extracted by using the synchrosqueezing transform and the reconstruction method. Afterward, the Lempel-Ziv complexity values are calculated. Since the complexity values are not dependent on the magnitude of the measured signal, the proposed method is less sensitive to the data sets measured under different data acquisition conditions. This approach is applied for monitoring and diagnosing the defects during a fatigue test on a first gear reducer and also when varying the load on a second gear reducer by using the recorded vibration signals. It can also provide a new way for feature extraction and recognition of gear system faults.