Multivariate sparse Bayesian learning for guided wave‐based multidamage localization in plate‐like structures

2022 ◽  
Author(s):  
Meijie Zhao ◽  
Yong Huang ◽  
Wensong Zhou ◽  
Hui Li
Keyword(s):  
Bayesian Learning ◽  
Guided Wave ◽  
Sparse Bayesian Learning

Guided-wave signal processing by the sparse Bayesian learning approach employing Gabor pulse model

2016 ◽  
Vol 16 (3) ◽  
pp. 347-362 ◽  
Author(s):  
Biao Wu ◽  
Yong Huang ◽  
Xiang Chen ◽  
Sridhar Krishnaswamy ◽  
Hui Li
Keyword(s):  
Damage Detection ◽  
Bayesian Learning ◽  
Guided Waves ◽  
Learning Algorithm ◽  
Guided Wave ◽  
Sparse Bayesian Learning ◽  
Mode Identification ◽  
Wave Signal ◽  
Gabor Dictionary ◽  
The Many

Guided waves have been used for structural health monitoring to detect damage or defects in structures. However, guided wave signals often involve multiple modes and noise. Extracting meaningful damage information from the received guided wave signal becomes very challenging, especially when some of the modes overlap. The aim of this study is to develop an effective way to deal with noisy guided-wave signals for damage detection as well as for de-noising. To achieve this goal, a robust sparse Bayesian learning algorithm is adopted. One of the many merits of this technique is its good performance against noise. First, a Gabor dictionary is designed based on the information of the noisy signal. Each atom of this dictionary is a modulated Gaussian pulse. Then the robust sparse Bayesian learning technique is used to efficiently decompose the guided wave signal. After signal decomposition, a two-step matching scheme is proposed to extract meaningful waveforms for damage detection and localization. Results from numerical simulations and experiments on isotropic aluminum plate structures are presented to verify the effectiveness of the proposed approach in mode identification and signal de-noising for damage detection.

Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves

2020 ◽  
pp. 147592172090227 ◽  
Author(s):  
Meijie Zhao ◽  
Wensong Zhou ◽  
Yong Huang ◽  
Hui Li
Keyword(s):  
Wave Packet ◽  
Structural Damage ◽  
Bayesian Learning ◽  
Learning Algorithm ◽  
Mode Conversion ◽  
Wave Packets ◽  
Propagation Distance ◽  
Guided Wave ◽  
Damage Localization ◽  
Sparse Bayesian Learning

In ultrasonic guided wave–based damage detection, the propagation distance recognition of wave packets is an essential step. However, it is difficult to perform direct distance extraction from guided wave signals since the multimode, mode conversion, and dispersion effects typically lead to wave packet overlapping and distortion. In addition, the identified damage location may be incorrect due to inevitable uncertainties in the procedure of propagation distance recognition and damage localization. Motivated by these difficulties, a novel two-stage approach for propagation distance recognition and damage localization is proposed based on sparse Bayesian learning framework. In the first stage, prior knowledge of a small number of wave packets contained in a signal is exploited to sparsely represent the guided wave signal and then the corresponding propagation distance and amplitude information of each wave packet can be obtained. In the next stage, only a small number of damages occurring in a structure are exploited and a vector consisting of the propagation distances extracted from the previous stage is used to match the atoms in a pre-defined over-complete distance dictionary matrix, to achieve our goal of localizing structural damage. Both procedures of the two stages are realized by the sparse Bayesian learning algorithm, which obtains the most probable value and the corresponding uncertainty. A sampling strategy is presented to transfer the uncertainty of the propagation distance recognition to the subsequent damage localization. Finally, the effectiveness of the proposed method is validated using numerical simulation and experimental investigation on aluminum plates. The proposed method is only valid for single damage localization in the present form, but it has the potential to be extended for multiple damage localization.

Blind Identification of Multichannel Systems Based on Sparse Bayesian Learning

2016 ◽  
Vol E99.B (12) ◽  
pp. 2614-2622 ◽  
Author(s):  
Kai ZHANG ◽  
Hongyi YU ◽  
Yunpeng HU ◽  
Zhixiang SHEN ◽  
Siyu TAO
Keyword(s):  
Bayesian Learning ◽  
Blind Identification ◽  
Sparse Bayesian Learning ◽  
Multichannel Systems

Sparse Bayesian learning algorithm for separable dictionaries

2021 ◽  
Vol 111 ◽  
pp. 102990
Author(s):  
Andra Băltoiu ◽  
Bogdan Dumitrescu
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Sparse Bayesian Learning

scholarly journals Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

NeuroImage ◽  
2021 ◽  
pp. 118309
Author(s):  
Ali Hashemi ◽  
Chang Cai ◽  
Gitta Kutyniok ◽  
Klaus-Robert Müller ◽  
Srikantan S. Nagarajan ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Bayesian Learning ◽  
Learning Algorithms ◽  
Sparse Bayesian Learning

Sparse Bayesian learning mechanism for research of audible noise in UHV transmission project

2010 ◽  
Author(s):  
Lin Niu ◽  
Jian-guo Zhao ◽  
Jian Yang ◽  
Feng Xie ◽  
Ke-jun Li
Keyword(s):  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Learning Mechanism

Speech Signal Recovery Using Block Sparse Bayesian Learning

2019 ◽  
Vol 45 (3) ◽  
pp. 1567-1579
Author(s):  
Irfan Ahmed ◽  
Aftab Khan ◽  
Nasir Ahmad ◽  
NasruMinallah ◽  
Hazrat Ali
Keyword(s):  
Speech Signal ◽  
Bayesian Learning ◽  
Signal Recovery ◽  
Sparse Bayesian Learning
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