Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach
This work introduces a novel methodology for identifying critical sensor locations and detecting defects in structural components. Initially, a hybrid method is proposed to determine optimal sensor placements by integrating results from both the discrete empirical interpolation method (DEIM) and the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/91 |
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| author | Minyoung Yun Mikhael Tannous Chady Ghnatios Eivind Fonn Trond Kvamsdal Francisco Chinesta |
| author_facet | Minyoung Yun Mikhael Tannous Chady Ghnatios Eivind Fonn Trond Kvamsdal Francisco Chinesta |
| author_sort | Minyoung Yun |
| collection | DOAJ |
| description | This work introduces a novel methodology for identifying critical sensor locations and detecting defects in structural components. Initially, a hybrid method is proposed to determine optimal sensor placements by integrating results from both the discrete empirical interpolation method (DEIM) and the random permutation features importance technique (PI). Subsequently, the identified sensors are utilized in a novel defect detection approach, leveraging a semi-intrusive reduced order modeling and genetic search algorithm for fast and reliable defect detection. The proposed algorithm has successfully located defects with low error, especially when using hybrid sensors, which combine the most critical sensors identified through both PI and DEIM. This hybrid method identifies defects with the lowest errors compared to using either the PI or DEIM methods alone. |
| format | Article |
| id | doaj-art-186d0d8e532d4ad88dd2c4fd99cad68a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-186d0d8e532d4ad88dd2c4fd99cad68a2025-01-10T13:20:50ZengMDPI AGSensors1424-82202024-12-012519110.3390/s25010091Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM ApproachMinyoung Yun0Mikhael Tannous1Chady Ghnatios2Eivind Fonn3Trond Kvamsdal4Francisco Chinesta5PIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, FrancePIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, FranceMechanical Engineering Department, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, USADepartment of Mathematics and Cybernetics, SINTEF Digital, Kloebuveien 153, 7465 Trondheim, NorwayDepartment of Mathematics and Cybernetics, SINTEF Digital, Kloebuveien 153, 7465 Trondheim, NorwayPIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, FranceThis work introduces a novel methodology for identifying critical sensor locations and detecting defects in structural components. Initially, a hybrid method is proposed to determine optimal sensor placements by integrating results from both the discrete empirical interpolation method (DEIM) and the random permutation features importance technique (PI). Subsequently, the identified sensors are utilized in a novel defect detection approach, leveraging a semi-intrusive reduced order modeling and genetic search algorithm for fast and reliable defect detection. The proposed algorithm has successfully located defects with low error, especially when using hybrid sensors, which combine the most critical sensors identified through both PI and DEIM. This hybrid method identifies defects with the lowest errors compared to using either the PI or DEIM methods alone.https://www.mdpi.com/1424-8220/25/1/91random permutation features importance methodoptimal sensor placementdiscrete empirical interpolation methodmachine learning |
| spellingShingle | Minyoung Yun Mikhael Tannous Chady Ghnatios Eivind Fonn Trond Kvamsdal Francisco Chinesta Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach Sensors random permutation features importance method optimal sensor placement discrete empirical interpolation method machine learning |
| title | Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach |
| title_full | Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach |
| title_fullStr | Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach |
| title_full_unstemmed | Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach |
| title_short | Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach |
| title_sort | enhanced sensor placement optimization and defect detection in structural health monitoring using hybrid pi deim approach |
| topic | random permutation features importance method optimal sensor placement discrete empirical interpolation method machine learning |
| url | https://www.mdpi.com/1424-8220/25/1/91 |
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