Blind Source Separation Method for Bearing Vibration Signals

In underdetermined blind source separation (UBSS) of vibration signals, the estimation of the mixing matrix is often affected by noise and by the type of the used clustering algorithm. A novel UBSS method for the analysis of vibration signals, aiming to address the problem of the inaccurate estimation of the mixing matrix owing to noise and choice of the clustering method, is proposed here. The proposed algorithm is based on the modified k-means clustering algorithm and the Laplace potential function. First, the largest distance between data points is used to initialize the cluster centroid locations, and then the mean distance between clustering centroids average distance range of data points is used for updating the locations of cluster centroids. Next, the Laplace potential function that uses a global similarity criterion is applied to fine-tune the cluster centroid locations. Normalized mean squared error and deviation angle measures were used to assess the accuracy of the estimation of the mixing matrix. Bearing vibration data from Case Western Reserve University and our experimental platform were used to analyze the performance of the developed algorithm. Results of this analysis suggest that this proposed method can estimate the mixing matrix more effectively, compared with existing methods.