Please use this identifier to cite or link to this item:
doi:10.22028/D291-35227
Title: | Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves |
Author(s): | Schnur, Christopher Goodarzi, Payman Lugovtsova, Yevgeniya Bulling, Jannis Prager, Jens Tschöke, Kilian Moll, Jochen Schütze, Andreas Schneider, Tizian |
Language: | English |
Title: | Sensors |
Volume: | 22 |
Issue: | 1 |
Publisher/Platform: | MDPI |
Year of Publication: | 2022 |
Free key words: | composite structures structural health monitoring carbon fibre-reinforced plastic interpretable machine learning automotive industry |
DDC notations: | 500 Science |
Publikation type: | Journal Article |
Abstract: | 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. |
DOI of the first publication: | 10.3390/s22010406 |
Link to this record: | urn:nbn:de:bsz:291--ds-352272 hdl:20.500.11880/32199 http://dx.doi.org/10.22028/D291-35227 |
ISSN: | 1424-8220 |
Date of registration: | 18-Jan-2022 |
Faculty: | NT - Naturwissenschaftlich- Technische Fakultät |
Department: | NT - Systems Engineering |
Professorship: | NT - Prof. Dr. Andreas Schütze |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Files for this record:
File | Description | Size | Format | |
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sensors-22-00406-v2.pdf | 4,48 MB | Adobe PDF | View/Open |
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