Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge
The usage of anomaly detection is of critical importance to numerous domains, including structural health monitoring (SHM). In this study, we examine an online setting for damage detection in the Z24 bridge. We evaluate and compare the performance of the elliptic envelope, incremental one-class supp...
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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/1424-8220/24/23/7866 |
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| author | Amro Abdrabo |
| author_facet | Amro Abdrabo |
| author_sort | Amro Abdrabo |
| collection | DOAJ |
| description | The usage of anomaly detection is of critical importance to numerous domains, including structural health monitoring (SHM). In this study, we examine an online setting for damage detection in the Z24 bridge. We evaluate and compare the performance of the elliptic envelope, incremental one-class support vector classification, local outlier factor, half-space trees, and entropy-guided envelopes. Our findings demonstrate that XGBoost exhibits enhanced performance in identifying a limited set of significant features. Additionally, we present a novel approach to manage drift through the application of entropy measures to structural state instances. The study is the first to assess the applicability of one-class classification for anomaly detection on the short-term structural health data of the Z24 bridge. |
| format | Article |
| id | doaj-art-88d7ab1684cc495cac890169e4ba2d8d |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-88d7ab1684cc495cac890169e4ba2d8d2025-08-20T01:55:30ZengMDPI AGSensors1424-82202024-12-012423786610.3390/s24237866Application of Online Anomaly Detection Using One-Class Classification to the Z24 BridgeAmro Abdrabo0Department of Computer Science, ETH Zürich, 8092 Zurich, SwitzerlandThe usage of anomaly detection is of critical importance to numerous domains, including structural health monitoring (SHM). In this study, we examine an online setting for damage detection in the Z24 bridge. We evaluate and compare the performance of the elliptic envelope, incremental one-class support vector classification, local outlier factor, half-space trees, and entropy-guided envelopes. Our findings demonstrate that XGBoost exhibits enhanced performance in identifying a limited set of significant features. Additionally, we present a novel approach to manage drift through the application of entropy measures to structural state instances. The study is the first to assess the applicability of one-class classification for anomaly detection on the short-term structural health data of the Z24 bridge.https://www.mdpi.com/1424-8220/24/23/7866structural health monitoringonline anomaly detectionsupervised feature extractionconcept drift |
| spellingShingle | Amro Abdrabo Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge Sensors structural health monitoring online anomaly detection supervised feature extraction concept drift |
| title | Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge |
| title_full | Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge |
| title_fullStr | Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge |
| title_full_unstemmed | Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge |
| title_short | Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge |
| title_sort | application of online anomaly detection using one class classification to the z24 bridge |
| topic | structural health monitoring online anomaly detection supervised feature extraction concept drift |
| url | https://www.mdpi.com/1424-8220/24/23/7866 |
| work_keys_str_mv | AT amroabdrabo applicationofonlineanomalydetectionusingoneclassclassificationtothez24bridge |