INDUCTION MOTOR BEARING FAULTS DIAGNOSIS USING AN IMPROVED KURTOGRAM METHOD (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2026)
It is well known that the bearing’s outer race is the most failing part of the rolling-element bearings used in induction motors, which causes, in most cases, the complete shutdown of the entire industrial process. Moreover, the early diagnosis of this fault is essential to improve the operational reliability of these motors and in order to avoid huge financial losses. In this aim, demodulating the stator current envelope is a promising diagnostic approach, allowing direct extraction of the fault signature without being affected by the fundamental frequency. In addition, the Kurtogram, a statistical tool of 4th order spectral analysis, makes it possible to extract the signature of the searched faults even in the case of non-stationary signals. Therefore, the purpose of this paper is to address a comparative study between two Kurtogram-based computation algorithms: Fast-Kurtogram and Wavelet-Kurtogram. The experimental results obtained by the stator current spectral analysis show the superiority of the Wavelet-Kurtogram compared to the Fast-Kurtogram in the detection and localization of the outer race fault.
Keywords: Induction motor; Stator current; Rolling-elements bearing; Fault diagnosis; Spectral kurtosis; Kurtogram