Time-Frequency Spectral Power Assessment of Rolling Element Bearing Faults Using Adaptive Modified Morlet Wavelet Transform

Abstract

Objectives: The main objectives of this manuscript are to investigate and diagnose rolling element bearing defects in its inception time. Methods: Vibration signal generated by the induction motor contains a series of frequency components that have rich and viable information about bearing health conditions. Recently, the Maximum Energy Concentration (MEC), measure of time-frequency spectrum has been employed to investigate the small variations in low frequency biomedical signal spectrum. In this paper, the above technique has been modified and applied to study the bearing defects of induction motor using vibration signal and it is termed as Adaptive Modified Morlet Wavelet (AMMW) transform. Initially, this proposed method was validated on two medium frequency synthetic time series signals in terms of MEC measurement at different Signal to Noise Ratio (SNR). Results: The simulated results have depicted that the AMMW method provides excellent timefrequency localization capability over other time-frequency methods like Morlet wavelet transform, modified Morlet wavelet transform, adaptive S-transform and adaptive modified S-transform. Then, this method has been applied to the standard database of vibration signal to determine interquartile power for fault detection purpose and also fault index parameter termed as IFI has been analyzed to detect small variation in vibration signals.