Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization

Abstract

The incipient bearing fault diagnosis is crucial to the industrial machinery maintenance. Developed based on the blind source separation, blind source extraction (BSE) has recently become the focus of intensive research work. However, owing to certain industrial restrictions, the number of sensors is usually less than that of the source signals, which is defined as an underdetermined BSE problem to identify the fault signals. The kernelized methods are found to be robust to the noise, especially in the presence of outliers, which makes it a suitable tool to extract fault signatures submerged in the strong environment noise. Thus, this paper proposes a new underdetermined BSE method based on the empirical mean decomposition and kernelized correlation. The experimental results indicate that the extracted fault signature presents more obvious periodicity. Two important parameters of this method, including the multi-shift number and the kernel size are investigated to improve the algorithm performance. Furthermore, performance comparisons with underdetermined BSE based on the second order correlation are made to emphasize the advantage of the presented method. The application of the proposed method is validated using the simulated signal and the rolling element bearing signal of the train vehicle axle.