作者：
Wu, BiaoZhou, WensongNanjing Tech Univ
Coll Civil Engn Nanjing 211816 Peoples R China Harbin Inst Technol
Sch Civil Engn Key Lab Struct Dynam Behav & Control Minist Educ Harbin 150090 Peoples R China Harbin Inst Technol
Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disaste Harbin 150090 Peoples R China
Ultrasonic testing has been an important tool for the detection of hidden defects in materials yet its effectiveness is usually compromised by the noise originated from both the testing system and the material being e...
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Ultrasonic testing has been an important tool for the detection of hidden defects in materials yet its effectiveness is usually compromised by the noise originated from both the testing system and the material being evaluated. Motivated by the prior knowledge that only a small number of defects or reflectors exist within the tested material thus the measured signal can be viewed as a linear combination of a few echoes, a signal processing method is proposed in this study where a nonconvex sparse regularization method based on l(p)-norm (0 < p < 1) penalty is employed to suppress noises in ultrasonic NDT signals thus enhancing the effectiveness and accuracy of flaw detection. Based on a specially designed overcomplete Gabor dictionary, the nonconvex sparse regularization method is introduced to sparsely represent the noisy signal. After signal representation, noise is suppressed by a pruning operator, facilitating the identification and reconstruction of flaw-reflected echoes. The performance of the proposed method is quantitatively evaluated and compared with competing algorithms using simulated noisy ultrasonic signals in a statistical manner. Experimental results are also presented to demonstrate the effectiveness of our method.
Recently, research interests are increasing in mode detection methods based on compressed sensing, as it can reduce the number of sensors required by the classical Shannon-Nyquist sampling theory. This paper proposes ...
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Recently, research interests are increasing in mode detection methods based on compressed sensing, as it can reduce the number of sensors required by the classical Shannon-Nyquist sampling theory. This paper proposes a nonconvex sparse regularization method for azimuthal mode detection for aero engine fan noise. The nonconvex sparse regularization is based on the generalized minimax-concave (GMC) penalty, which can maintain the convexity of the sparse-regularized least squares cost function, and thus the global optimal solution can be solved by convex optimization algorithms. The main advantage of the GMC method over conventional compressed sensing method in mode detection is that the GMC method can better recover the mode amplitudes with a small number of sensors. Besides, the GMC method can suppress effectively the irrelated modes induced by the background noise or sensor installation errors. Therefore, the proposed method for duct mode detection can significantly improve the accuracy of the detected modes. Numerical simulations and experimental tests verify the effectiveness of the GMC method in mode detection for aero engine fan noise, and comparison studies show that the GMC method provides more accurate mode detection results than l(1) minimization in the category of compressed sensing, as well as traditional mode detection methods. (C) 2020 Elsevier Ltd. All rights reserved.
sparse priors for signals play a key role in sparse signal modeling, and sparsity-assisted signal processing techniques have been studied widely for machinery fault diagnosis. In this paper, synthesis and analysis pri...
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sparse priors for signals play a key role in sparse signal modeling, and sparsity-assisted signal processing techniques have been studied widely for machinery fault diagnosis. In this paper, synthesis and analysis priors are introduced for sparseregularization problems via the generalized minimax-concave (GMC) penalty to improve the performance of signal denoising or signal decomposition for the purpose of machinery fault diagnosis. Firstly, the GMC-synthesis and GMC-analysis methods are proposed simultaneously for sparseregularization. Secondly, the gap between GMC-synthesis and GMC-analysis is explored systematically via theoretical and numerical analysis, especially via comparing the performance of GMC-synthesis and GMC-analysis for machinery fault diagnosis, including bearing fault diagnosis and gearbox fault diagnosis. Thirdly, a majorization-minimization-like (MM-like) algorithm is proposed to solve the optimization problem of GMC-synthesis and GMC-analysis. Furthermore, the early stop criterion and the adaptive strategy for regularization parameter selection is also provided in this paper. The results of the numerical simulation, experiment verification, and practical applications show that GMC-synthesis performs better for fault feature extraction than GMC-analysis and the other methods, including l(1)-synthesis, l(1)-analysis, and spectral kurtosis. (C) 2019 Elsevier Ltd. All rights reserved.
The latest tensor recovery methods based on tensor Singular Value Decomposition (t-SVD) mainly utilize the tensor nuclear norm (TNN) as a convex surrogate of the rank function. However, TNN minimization treats each ra...
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ISBN:
(纸本)9781665405409
The latest tensor recovery methods based on tensor Singular Value Decomposition (t-SVD) mainly utilize the tensor nuclear norm (TNN) as a convex surrogate of the rank function. However, TNN minimization treats each rank component equally and tends to over-shrink the dominant ones, thereby usually leading to biased solutions. To handle this critical issue, we put forward a weighted tensor Schantten-p (0 < p <= 1) norm (WTSN) as high-order tensor rank's more flexible nonconvex relaxation. Furthermore, another nonconvex l(q) (0 < q <= 1) sparseregularization item on the extensively existed noises/outliers is incorporated into the WSTN minimization to enhance its robustness in the impulsive scenarios. Finally, we propose an efficient and scalable robust high-order tensor recovery method solving a double nonconvex optimization with convergence guarantees. Synthetic and real experiments demonstrate that the proposed approach outperforms the state-of-the-art ones in terms of both accuracy and computational complexity.
sparseregularization has been attracting much attention in industrial applications over the past few decades. By exploiting the latent data structure in low-dimensional subspaces, a significant amount of research ach...
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sparseregularization has been attracting much attention in industrial applications over the past few decades. By exploiting the latent data structure in low-dimensional subspaces, a significant amount of research achievements have been realized in signal/image processing, pattern recognition and system identification, etc. However, very few systematic review or comprehensive survey are reported for sparseregularization including fundamentals, state-of-the-art methodologies, and applications on fault diagnosis. To fill this gap, this article conducts an in-depth review of the state-of-the-art technologies of sparseregularization, and the R & D of sparse regulariza-tion applied to fault diagnosis will also be summarized. Specifically, we discuss the rationales of cause formu-lation, algorithm idea, algorithm merits, algorithm demerits and computing techniques for each category. The availability and practicability of several representative models of sparseregularization are investigated with real-world experimental datasets. Finally, benefiting from theoretical developments of the sparseregularization, open/upcoming challenges, instructive perspectives, as well as possible future trends of the sparseregularization for prognostic and health management (PHM) are discussed.
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