A joint wavelet lifting and independent component analysis approach to fault detection of rolling element bearings

Though wavelet transforms have been used to extract bearing fault signatures from vibration signals in the literature, detection results often rely on a proper wavelet function and deep wavelet decomposition. The selection of a proper wavelet function is time consuming and deep decomposition demands more computing effort. This is unsuitable for on-line fault detection. As such, we propose a joint wavelet lifting scheme and independent component analysis (ICA) approach to detecting weak signatures of bearing faults. The optimal envelope spectrum of independent components for signature extraction is selected based on the maximum energy and total energy of each independent component. The performance of the proposed method is evaluated by comparing with several other methods using both simulated and real vibration signals. The results reveal that the proposed method is more effective and robust in extracting bearing fault signatures. The following advantages of the proposed method have also been observed: (a) it is insensitive to wavelet selection and hence is less susceptible to ill selected wavelet function; (b) it is insensitive to the depth of wavelet decomposition, leading to an efficient algorithm; and (c) it takes advantage of ICA in fault detection without using multiple sensors as required in the original ICA.