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Non-stationary vibration signal analysis of rotating machinery via time-frequency and wavelet techniques

This paper introduces an efficient and powerful approach to fault detection in rotating machinery using time-frequency analysis based on both Fourier and wavelet transforms of the monitored vibration signal. Time-frequency techniques are powerful tools for analyzing transient information in vibration signature for both condition monitoring and fault detection purposes. Our work on fault detection reported in this paper is two-fold: (1) application of the short-time Fourier transforms (STFT) and the exploitation of the spectrogram-based time-frequency distribution to detect various mechanical faults during the start-up & coast down phases in rotating machinery and (2) application of a novel wavelet-based technique combining both the continuous wavelet and the wavelet packet transforms. This novel technique exploits the use of the modulus of the local maxima lines in the wavelet domain, to detect impulsive mechanical faults such as impact blade-to-stator rubbing in turbo machinery. Both the analysis and the extensive simulation work carried out here show in particular the superiority of our proposed combined wavelet-based approach over the traditional Fourier Transform (FFT) method, in reliably diagnosing impulsive mechanical faults and start-up and cost down signals.