Induction Motor's Bearing Fault Diagnosis Using an Improved Short Time Fourier Transform

摘要

Induction motor diagnosis using the Power Spectral Density estimation or PSD, based on the Fourier Transform calculation, is not recommended for the processing of non stationary signals (case of variable speed applications). In fact, under these conditions, the analysis with this approach is no more reliable. To resolve this, we use in this paper, the Short Time Fourier Transform (STFT), to obtain information on changes of the frequencies over time. Furthermore, we propose the use of a new approach called Maxima's Location Algorithm (MLA) which will be associated to the STFT analysis to show only 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. Experimental results show the merits of the proposed approach on the reliability of the bearing fault detection.