Abstract
In the progress of bearing fault acoustic testing, signals picked up by acoustic sensors are usually mixed with fault source signals and other noise signals due to the complexity of mechanical signals and various interference sources. In order to solve the above problems, an improved blind deconvolution algorithm is put forward. The proposed algorithm applies adaptive generalized morphological filtering to the observed signals to retain their characteristic details, and then utilizes an OMP algorithm based on the minimum kurtosis to restore the periodical signals in the mixed signals in order to reduce the impact of the periodic components on blind separation. Finally, the improved Kullback–Leibler (KL) distance algorithm is employed to calculate the distances between independent components, which is used as the clustering index, and then to perform fuzzy C-means clustering. The experiment results of bearing compound fault extraction in real working-environment demonstrate the accuracy and reliability of the proposed algorithm.
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