Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks

Guided wave structural health monitoring (GWSHM) systems, using the delay-and-sum imaging algorithm, are an efficient solution to detect and localise defects in industrial structures. However, the image artifacts caused by either imperfect detection or sensor lay-out limitations make it difficult to identify and locate defects accurately. In order to enhance the performance of defect detection and localisation in GWSHM systems, this paper proposes a three-step procedure for post-processing guided wave signals prior to image formation. In the first step, the signals are processed using the nonlinear autoregressive exogenous model to suppress noise from benign features. The second step calculates the probability of defect presence based on the rescaled Gamma cumulative distribution function. This probabilistic threshold is then determined from the quantile mapping. Finally, a guide wave image is formed using the delay-and-sum imaging algorithm. The experimental validation was performed to inspect a 6 mm-diameter through-thickness circular hole on an aluminium plate and the defects were further scaled as simulated datasets to test its detectability under various amplitudes. In the second procedure step, the detection and localisation performance of the proposed procedure was compared with that of using the signal difference coefficient and the Rayleigh maximum likelihood estimator. It is shown that the proposed procedure can enhance the contrast between damaged and undamaged regions, providing more reliable and accurate guided wave images.

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Guided wave-driven machine learning for damage classification with limited dataset in aluminum panel

Rezazadeh,

Perfetto,

Polverino

et al. 2024

Structural Health Monitoring

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The goal of this research study is to classify damages in an aluminum panel using ultrasonic-guided waves (UGWs). The study is carried out on the premise that only a limited dataset, consisting of only two receivers, is available. In the experimental phase, three distinct parts, that is, stainless steel masses, were located on the surface of the panel, assumed to be mapped into four triangular regions, to experimentally simulate the effects induced by circular artificial damage of varied sizes. UGW-driven damage classification was thus performed via machine learning. A data synthesis technique called conditional generative adversarial network (cGAN) was used to generate 2000 samples (signals) for each of the 12 different malfunction scenarios (classes), based on the combination of damage region and size. Damage features were extracted using the wavelet time scattering technique, while the classification task was performed via a long short-term memory network with tuned hyperparameters. A validation procedure was executed to ensure that the classification network was not overfitted. Furthermore, two testing stages were performed to evaluate the designed framework. The designed framework was thus tested on the observations excluded from the training and validation phases, as well as on 10 additional novel experimental samples. The high accuracy and F1-score values of the validation and the two testing stages—97.7%, 98.5%, and 96.6%, respectively—attest to the generality of this methodology, which is neither underfitted nor overfitted. To further assess the robustness of the methodology, the cGAN was tested on a new sensor placement, demonstrating a consistent performance (95.3%) and confirming the framework applicability under different sensor configurations, showcasing potential extension to other classification problems featuring a limited dataset.

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Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks

Cited by 5 publications

References 62 publications

Bayesian-Network-Based Predictions of Water Inrush Incidents in Soft Rock Tunnels

Bayesian-Network-Based Predictions of Water Inrush Incidents in Soft Rock Tunnels

Defect detection and localisation using guided wave images from array data processed by nonlinear autoregressive exogenous model and Gamma statistical operator

Guided wave-driven machine learning for damage classification with limited dataset in aluminum panel

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