Convolutive blind separation of bearing faults using peak-based wavelet transform

In the health monitoring of rotating machinery, there often coexists multiple fault sources; thus a multi-source composite fault signal will be collected by sensors. Moreover, the vibration signal of rotating machinery is usually submerged by the environmental noise in practical engineering occasions. These increase the difficulty of separation from the composite fault signal. However, the effect of traditional wavelet analysis method for denoising to enhance features of fault signal is limited, because of the energy dispersion of fault signals. To overcome these problems, we propose a convolutive blind separation method based on peak transform using wavelet analysis. With this strategy, the original signal energy in high frequency is concentrated into that in low frequency band, through some non-linear transformations. Next, the large wavelet coefficients, which represent the main features of the signal, are retained to reconstruct a feature-enhanced signal. The convolutive fixed-point algorithm based on maximization of non-Gaussianity, is carried out to separate source signals from the feature-enhanced signal. Experimental results of rolling bearing show that the proposed strategy can effectively separate source fault signals from the composite signal and detect the fault features.