Adaptive fault identification of bearing using empirical mode decomposition–principal component analysis-based average kurtosis technique

Abstract

The kinematics of the bearing is erratic and random in nature and requires timely attention to avoid any catastrophic failure. In this study, the authors have proposed and analysed the amplitude and frequency modulated signals emanating from the bearing using four steps, i.e. standardisation, empirical mode decomposition, principal component analysis (PCA), envelope and cepstral envelope techniques. First, the standardised frequency modulated signals are decomposed into stationary non-linear modes called intrinsic mode functions (IMFs). In this approach, PCA is applied on the decomposed IMFs to produce uncorrelated signals. The uncorrelated signals whose value is above the average kurtosis are recombined to form a modified signal. The modified signal incurred from the approach is followed by spectrum, envelope, cepstrum, and cepstral envelope techniques to identify the features. It is observed this proposed combined approach effectively and adaptively identifies the inner/ball faults, shaft rotating frequency and corresponding harmonics in ease with least utilisation of IMFs.