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.
Highlights
In the field of mechanical fault diagnosis, vibration signals always contain a wealth of information about equipment operating status
We can obtain the correlation matrix about the new observation matrix, and the singular value decomposition of the According to Figure 4d, we can find that the characteristics of two constituted signals in the time-domain cannot be clearly indicated under the strong background noise
The research work reported in this paper has two main contributions: firstly, a novel single channel is proposed, which can method obtain ausing bettertensor-based convergencesingular of tensor decomposition a higher reliable blind source separation spectrum analysisand based on CP-WOPT
Summary
In the field of mechanical fault diagnosis, vibration signals always contain a wealth of information about equipment operating status. In the study of complex fault diagnosis under single channel condition, the general solution to this problem is not unique and various approaches have been proposed, ranging from applying independence assumptions to non-negativity and sparsity constraints [12]. Research in this area mainly focuses on the virtual multi-channel method. The tensor-based singular spectrum analysis (TSSA) algorithm, which provides an effective way for solving the above problem of SCBSS, was proposed by Saeid et al [26] and has been applied to the field of EEG signal processing.
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