NDLI: Blind Source Separation Method for Bearing Vibration Signals

Content Provider	Directory of Open Access Journals (DOAJ)
Author	He Jun Yong Chen Qing-Hua Zhang Guoxi Sun Qin Hu
Abstract	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.
e-ISSN	21693536
DOI	10.1109/ACCESS.2017.2773665
Journal	IEEE Access
Volume Number	6
Language	English
Publisher	IEEE
Publisher Date	2018-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Signal Underdetermined Blind Source Separation Laplace Potential Function K-means Bearing Vibration Signal
Content Type	Text
Resource Type	Article

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