Efficient Lamb-wave based damage imaging using multiple sparse Bayesian learning in composite laminates

Zhang, Han; Hua, Jiadong; Gao, Fei; Lin, Jing

doi:10.1016/j.ndteint.2020.102277

Cited by 40 publications

(15 citation statements)

References 42 publications

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“…In this case, toneburst-based dictionary atoms are designed for sparse representation. 23,35,36 The convolution kernel in the first convolutional layer is similar to the dictionary in sparse representation theoretically. Therefore, in this article, the first convolutional layer is enhanced with these toneburst signals, which makes the model discover features of Lamb waves in the frequency range of interest.…”

Section: Lamb Wave Convolution Kernelsmentioning

confidence: 99%

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Zhang

Lin

Hua

et al. 2021

Structural Health Monitoring

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Lamb wave-based damage identification and localization methods hold the potential for nondestructive evaluation and structural health monitoring. Dispersive and multimodal characteristics lead to complicated Lamb wave signals that are challenging to be analyzed. Deep learning architectures could identify damage-related features effectively. Convolutional neural network (CNN) is a promising architecture that has been widely applied in recent years. However, this data-driven approach still lacks a certain degree of physical interpretability and requires a large number of parameters. In this article, an interpretable Lamb wave convolutional sparse coding (LW-CSC) method is proposed for structural damage identification and localization. First, toneburst signals at different center frequencies are considered in the first convolutional layer. The network convolves the waveforms with a set of parametrized functions that implement band-pass filters, which performs more physical interpretability compared to conventional CNN model. Subsequently, the damage features are extracted according to the multi-layer iterative soft thresholding algorithm for multi-layer CSC model, which could realize a deeper network without adding parameters unlike CNN. Finally, Lamb wave-based damage localization is visualized using an imaging algorithm. The experimental results demonstrate that the proposed method not only enables improvement of the classification accuracy but also achieves structural damage localization accurately.

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Section: Lamb Wave Convolution Kernelsmentioning

confidence: 99%

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Zhang

Lin

Hua

et al. 2021

Structural Health Monitoring

Self Cite

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“…Zhou et al 40 proposed a novel two-stage approach for propagation distance recognition and damage localization based on SBL framework. In addition to the application in the field of structural health detection, SBL is also used in model selection, compressed sensing, damage imaging, etc., [41][42][43][44] showing its strong applicability.…”

Section: Introductionmentioning

confidence: 99%

Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme

Wang

Yang

et al. 2022

Structural Contr & Hlth

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Despite continuous evolution and development of structural health monitoring (SHM) technology, interpreting a huge amount of sensed data from a sophisticated SHM system to extract useful information about structural health condition remains a challenge. Aiming to resolve this problem, a novel application of probabilistic data-driven damage detection method was proposed in the context of Sparse Bayesian Learning (SBL) scheme. The framework involves constructing a new structural damage index and establishing SBL regression model as reference base only using the data acquired in health state. The construction of the structural damage index is based on damage-sensitive frequency band, which is determined by NExT using vibration monitoring data. The structure will be classified to be damaged as the structural damage index based on new data deviates from the index predicted by SBL regression reference model, and further, the Bayes factor is adopted to quantify the damage degree. In addition, the relationship between the Bayes factors and the resonance frequency change rate is investigated in detail. The proposed methodology features the following merits: (i) It is probabilistic data-driven method exempting from physical model of the structure, excitation/loading information, and (ii) it belongs to the unsupervised model in need for structural damage detection, which can be formulated using only monitoring data from health state in the absence of monitoring data from damaged state. Damage detection and discrimination capabilities of the proposed methodology are verified using field monitoring data acquired from a cable-stayed bridge. Finally, a discussion of the SBL-based approach is made and further challenges pertaining to damage detection processes in the context of SHM are identified.

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“…In real operating conditions, uncertainty in some parameters cause model mismatch in SHM systems, such as existing unpredictable noise and lack of information about the exact mode of the Lamb wave propagation, while others reduce the quality of comparison for example temperature variation and humidity. Despite applying the compensation methods described in [34,[37][38][39][40] and some methods for signal treatments such as sparse Bayesian learning [41,42], these factors continue to cause challenges and to decrease the accuracy and reliability of SHM methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the sparse reconstruction and the delay-and-sum damage imaging methods for structural health monitoring under different environmental and operational conditions

2021

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Efficient Lamb-wave based damage imaging using multiple sparse Bayesian learning in composite laminates

Cited by 40 publications

References 42 publications

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme

Evaluation of the sparse reconstruction and the delay-and-sum damage imaging methods for structural health monitoring under different environmental and operational conditions

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