Abstract
Mechanical vibration signal mapped into a high-dimensional space tends to exhibit a special distribution and movement characteristics, which can further reveal the dynamic behavior of the original time series. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, the tensor decomposition algorithm has broad application prospects in signal processing. High-dimensional tensor can be obtained from a one-dimensional vibration signal by using phase space reconstruction, which is called the tensorization of data. As a new signal decomposition method, tensor-based singular spectrum algorithm (TSSA) fully combines the advantages of phase space reconstruction and tensor decomposition. However, TSSA has some problems, mainly in estimating the rank of tensor and selecting the optimal reconstruction tensor. In this paper, the improved TSSA algorithm based on convex-optimization and permutation entropy (PE) is proposed. Firstly, aiming to accurately estimate the rank of tensor decomposition, this paper presents a convex optimization algorithm using non-convex penalty functions based on singular value decomposition (SVD). Then, PE is employed to evaluate the desired tensor and improve the denoising performance. In order to verify the effectiveness of proposed algorithm, both numerical simulation and experimental bearing failure data are analyzed.
Highlights
IntroductionRolling bearing is one of the most widely used and damaged rotating machine elements, the operation status of which is directly related to the operation of machinery and production efficiency
Rolling bearing is one of the most widely used and damaged rotating machine elements, the operation status of which is directly related to the operation of machinery and production efficiency.it has a very high practical value to achieve accurate diagnosis and recognition of rolling bearing fault
The permutation entropy (PE) has the advantages of simple calculation, strong anti-noise ability, and high sensitivity occur in gear and bearing parts during operation process, the influence of the nonlinear factors and to signal change
Summary
Rolling bearing is one of the most widely used and damaged rotating machine elements, the operation status of which is directly related to the operation of machinery and production efficiency. An improved TSSA decomposition method is proposed by applying the convex optimization for the rank estimation and permutation entropy (PE) for desired tensor selection. The PE has the advantages of simple calculation, strong anti-noise ability, and high sensitivity occur in gear and bearing parts during operation process, the influence of the nonlinear factors and to signal change. PE is employed as novel approach to select bearing parts during operation process, the influence of the nonlinear factors and signal complexity the desired tensor for reconstruction in this paper. PE is employed as novel approach to select the desired tensor proposed method is compared with the traditional singular spectrum analysis (SSA) and EMD. The structure of this paper is arranged as follows: In Section 2, tensor singular spectrum vibration signal and fault simulation test bed are used for analysis.
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