{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:28:26Z","timestamp":1773808106226,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T00:00:00Z","timestamp":1641340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22010406","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6844-0382","authenticated-orcid":false,"given":"Christopher","family":"Schnur","sequence":"first","affiliation":[{"name":"Lab for Measurement Technology, Saarland University, 66123 Saarbr\u00fccken, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3937-1752","authenticated-orcid":false,"given":"Payman","family":"Goodarzi","sequence":"additional","affiliation":[{"name":"Lab for Measurement Technology, Saarland University, 66123 Saarbr\u00fccken, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5953-6331","authenticated-orcid":false,"given":"Yevgeniya","family":"Lugovtsova","sequence":"additional","affiliation":[{"name":"Department of Non-Destructive Testing, Acoustic and Electromagnetic Methods Division, Bundesanstalt f\u00fcr Materialforschung und -Pr\u00fcfung (BAM), 12205 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1284-9699","authenticated-orcid":false,"given":"Jannis","family":"Bulling","sequence":"additional","affiliation":[{"name":"Department of Non-Destructive Testing, Acoustic and Electromagnetic Methods Division, Bundesanstalt f\u00fcr Materialforschung und -Pr\u00fcfung (BAM), 12205 Berlin, Germany"}]},{"given":"Jens","family":"Prager","sequence":"additional","affiliation":[{"name":"Department of Non-Destructive Testing, Acoustic and Electromagnetic Methods Division, Bundesanstalt f\u00fcr Materialforschung und -Pr\u00fcfung (BAM), 12205 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3971-1515","authenticated-orcid":false,"given":"Kilian","family":"Tsch\u00f6ke","sequence":"additional","affiliation":[{"name":"Systems for Condition Monitoring, Fraunhofer Institute for Ceramic Technologies and Systems IKTS, 01109 Dresden, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2299-2250","authenticated-orcid":false,"given":"Jochen","family":"Moll","sequence":"additional","affiliation":[{"name":"Department of Physics, Goethe University Frankfurt, 60438 Frankfurt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3060-5177","authenticated-orcid":false,"given":"Andreas","family":"Sch\u00fctze","sequence":"additional","affiliation":[{"name":"Lab for Measurement Technology, Saarland University, 66123 Saarbr\u00fccken, Germany"},{"name":"Research Group Data Engineering and Smart Sensors, ZeMA\u2014Center for Mechatronics and Automation Technology gGmbH, 66121 Saarbr\u00fccken, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3488-8944","authenticated-orcid":false,"given":"Tizian","family":"Schneider","sequence":"additional","affiliation":[{"name":"Lab for Measurement Technology, Saarland University, 66123 Saarbr\u00fccken, Germany"},{"name":"Research Group Data Engineering and Smart Sensors, ZeMA\u2014Center for Mechatronics and Automation Technology gGmbH, 66121 Saarbr\u00fccken, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.ymssp.2019.01.034","article-title":"Improved density peak clustering-based adaptive Gaussian mixture model for damage monitoring in aircraft structures under time-varying conditions","volume":"126","author":"Qiu","year":"2019","journal-title":"Mech. 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