Induction motor diagnosis using the Power Spectral Density (PSD) estimation based on the Fourier Transform calculation has been widely used as an analysis method for its simplicity and low computation time. However, the use of PSD is not recommended for processing non stationary signals (case of variable speed applications) and therefore the analysis with PSD is not reliable. To overcome this handicap, the Short Time Fourier Transform (STFT) is proposed in this paper; giving additional information on changes of the frequencies over time for stator current signal analysis. Furthermore, the use of a new approach called Maxima's Location Algorithm is also proposed. This later will be associated with the STFT analysis to show only those harmonics with useful information on existing faults. This approach will be used in the diagnosis of bearing faults of a PWM inverter-fed induction motor operating at variable speed. Several experimental results in the transient state are carried out firstly to validate the results and secondly to illustrate the merits and effectiveness of the combined STFT/ MLA proposed approach.

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This paper describes a new diagnosis approach, the Root-MUSIC (RM) method, for identification of the progressive cracking in the bearing of induction motors. This approach has several advantages compared to the stator current spectral analysis using the conventional Periodogram method. Indeed, the main advantage of this approach is its very good frequency resolution for a very short acquisition time, something impossible to achieve with the conventional method. However, in order to reduce the computation time which is the main drawback of the RM method, this method will be applied to only a specified frequency band; one that carries information about the sought fault. Experimental results show the effectiveness of RM method on the reliability of the incipient bearing fault detection.