Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition

This study reports a joint wavelet decomposition and Fourier transform approach to the separation of periodic mechanical source signals from single-channel signal mixture. With this method, the signal mixture is first decomposed to certain wavelet scales. The resulting wavelet coefficients are then Fourier transformed to extract the information pertinent to each signal source from these scales. Next, the number of signal sources is determined and the wavelet coefficients for each signal are constructed in all scale levels. Finally the source signals can be reconstructed using these wavelet coefficients. Since this method does not require the number of sources to be known a priori, it is particularly suitable for mechanical fault signal separation as the number of source signals varies with time and is unpredictable. It is also important to point out that the number of sources is determined without the commonly adopted sequential extraction/learning process and hence the proposed method can be used for on-line fault detection due to the reduced computing burden. The application of this method has been demonstrated using mixed bearing data containing both inner and outer race fault signals.