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

Ultrasonics
Mahindra RautelaSrinivasan Gopalakrishnan

Abstract

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 i...Continue Reading

References

Jan 3, 2019·The Journal of the Acoustical Society of America·Armin M A Huber, Markus G R Sause

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