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Appl. Sci. 2017, 7(4), 418; doi:10.3390/app7040418

Improved Tensor-Based Singular Spectrum Analysis Based on Single Channel Blind Source Separation Algorithm and Its Application to Fault Diagnosis

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1
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China
2
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
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The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China
4
State Key Lab. of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan 430081, China
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Author to whom correspondence should be addressed.
Academic Editor: César M. A. Vasques
Received: 9 March 2017 / Revised: 8 April 2017 / Accepted: 16 April 2017 / Published: 20 April 2017
(This article belongs to the Special Issue Structural Health Monitoring (SHM) of Civil Structures)
View Full-Text   |   Download PDF [6866 KB, uploaded 24 April 2017]   |  

Abstract

To solve the problem of multi-fault blind source separation (BSS) in the case that the observed signals are under-determined, a novel approach for single channel blind source separation (SCBSS) based on the improved tensor-based singular spectrum analysis (TSSA) is proposed. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, TSSA method can be employed to extract the multi-fault features from the measured single-channel vibration signal. However, SCBSS based on TSSA still has some limitations, mainly including unsatisfactory convergence of TSSA in many cases and the number of source signals is hard to accurately estimate. Therefore, the improved TSSA algorithm based on canonical decomposition and parallel factors (CANDECOMP/PARAFAC) weighted optimization, namely CP-WOPT, is proposed in this paper. CP-WOPT algorithm is applied to process the factor matrix using a first-order optimization approach instead of the original least square method in TSSA, so as to improve the convergence of this algorithm. In order to accurately estimate the number of the source signals in BSS, EMD-SVD-BIC (empirical mode decomposition—singular value decomposition—Bayesian information criterion) method, instead of the SVD in the conventional TSSA, is introduced. To validate the proposed method, we applied it to the analysis of the numerical simulation signal and the multi-fault rolling bearing signals. View Full-Text
Keywords: blind source separation (BSS); tensor-based singular spectrum analysis (TSSA); CANDECOMP/PARAFAC weighted optimization (CP-WOPT); fault diagnosis blind source separation (BSS); tensor-based singular spectrum analysis (TSSA); CANDECOMP/PARAFAC weighted optimization (CP-WOPT); fault diagnosis
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Yang, D.; Yi, C.; Xu, Z.; Zhang, Y.; Ge, M.; Liu, C. Improved Tensor-Based Singular Spectrum Analysis Based on Single Channel Blind Source Separation Algorithm and Its Application to Fault Diagnosis. Appl. Sci. 2017, 7, 418.

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