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Sensors 2016, 16(6), 897; doi:10.3390/s16060897

Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals

1
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
2
Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, China
*
Author to whom correspondence should be addressed.
Academic Editor: Piervincenzo Rizzo
Received: 16 April 2016 / Revised: 30 May 2016 / Accepted: 13 June 2016 / Published: 16 June 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2962 KB, uploaded 16 June 2016]   |  

Abstract

In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method. View Full-Text
Keywords: roller bearing; fault diagnosis; variational mode decomposition; independent component analysis roller bearing; fault diagnosis; variational mode decomposition; independent component analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tang, G.; Luo, G.; Zhang, W.; Yang, C.; Wang, H. Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals. Sensors 2016, 16, 897.

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