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Article

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

1
Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany
2
Department of Non-Destructive Testing, Acoustic and Electromagnetic Methods Division, Bundesanstalt für Materialforschung und -Prüfung (BAM), 12205 Berlin, Germany
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Systems for Condition Monitoring, Fraunhofer Institute for Ceramic Technologies and Systems IKTS, 01109 Dresden, Germany
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Department of Physics, Goethe University Frankfurt, 60438 Frankfurt, Germany
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Research Group Data Engineering and Smart Sensors, ZeMA—Center for Mechatronics and Automation Technology gGmbH, 66121 Saarbrücken, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Branko Glisic
Sensors 2022, 22(1), 406; https://doi.org/10.3390/s22010406
Received: 4 November 2021 / Revised: 21 December 2021 / Accepted: 29 December 2021 / Published: 5 January 2022
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. View Full-Text
Keywords: composite structures; structural health monitoring; carbon fibre-reinforced plastic; interpretable machine learning; automotive industry composite structures; structural health monitoring; carbon fibre-reinforced plastic; interpretable machine learning; automotive industry
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MDPI and ACS Style

Schnur, C.; Goodarzi, P.; Lugovtsova, Y.; Bulling, J.; Prager, J.; Tschöke, K.; Moll, J.; Schütze, A.; Schneider, T. Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves. Sensors 2022, 22, 406. https://doi.org/10.3390/s22010406

AMA Style

Schnur C, Goodarzi P, Lugovtsova Y, Bulling J, Prager J, Tschöke K, Moll J, Schütze A, Schneider T. Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves. Sensors. 2022; 22(1):406. https://doi.org/10.3390/s22010406

Chicago/Turabian Style

Schnur, Christopher, Payman Goodarzi, Yevgeniya Lugovtsova, Jannis Bulling, Jens Prager, Kilian Tschöke, Jochen Moll, Andreas Schütze, and Tizian Schneider. 2022. "Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves" Sensors 22, no. 1: 406. https://doi.org/10.3390/s22010406

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