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Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning

Rautela, M and Senthilnath, J and Moll, J and Gopalakrishnan, S (2021) Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning. In: Ultrasonics, 115 .

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Official URL: https://doi.org/10.1016/j.ultras.2021.106451


Structural Health Monitoring of composite structures is one of the significant challenges faced by the aerospace industry. A combined two-level damage identification viz damage detection and localization is performed in this paper for a composite panel using ultrasonic guided waves. A novel physical knowledge-assisted machine learning technique is proposed in which domain knowledge and expert supervision is utilized to assist the learning process. Two supervised learning-based convolutional neural networks are trained for damage detection (binary classification) and localization (multi-class classification) on an experimental benchmark dataset. The performance of the trained models is evaluated using loss curve, accuracy, confusion matrix, and receiver-operating characteristics curve. It is observed that incorporating physical knowledge helps networks perform better than a direct deep learning approach. In this work, a combined damage identification strategy is proposed for a real-time application. In this strategy, the damage detection model works in an outer-loop and predicts the state of the structure (undamaged or damaged), whereas an inner-loop predicts the location of the damage only if the outer-loop detects damage. It is seen that the proposed technique offers advantages in terms of accuracy (above 99 for both detection and localization), computational time (prediction time per signal in milliseconds), sensor optimization, in-situ monitoring, and robustness towards the noise. © 2021 Elsevier B.V.

Item Type: Journal Article
Publication: Ultrasonics
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to Elsevier B.V.
Keywords: Aerospace industry; Classification (of information); Composite structures; Convolutional neural networks; Deep learning; Guided electromagnetic wave propagation; Learning systems; Structural health monitoring; Ultrasonic applications; Ultrasonic waves, Binary classification; Damage Identification; Detection and localization; Experimental benchmarks; Machine learning techniques; Multi-class classification; Receiver operating characteristics; Ultrasonic guided wave, Damage detection
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 03 Aug 2021 10:13
Last Modified: 03 Aug 2021 10:13
URI: http://eprints.iisc.ac.in/id/eprint/68908

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