Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation
The detection of acoustic emission events from various failing mechanisms, such as plastic deformations, is a critical element in the monitoring and timely detection of structural failures in infrastructures. This study focuses on the detection of such failures in metal gates at rivers’ lifting dams...
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MDPI AG
2025-02-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/2/38 |
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| author | Theocharis Tsenis Vassilios Kappatos |
| author_facet | Theocharis Tsenis Vassilios Kappatos |
| author_sort | Theocharis Tsenis |
| collection | DOAJ |
| description | The detection of acoustic emission events from various failing mechanisms, such as plastic deformations, is a critical element in the monitoring and timely detection of structural failures in infrastructures. This study focuses on the detection of such failures in metal gates at rivers’ lifting dams aiming to increase the reliability of river transport compared to the current situation, thereby, increasing the resilience of transport corridors. During our study, we used lifting dams in both France and Italy where river transport is thriving. A methodology was developed, processing corresponding acoustic emission recordings originating from lifting dams’ metal gates, using advanced denoising—preprocessing, various decompositions, and spectral embeddings associated with various latest nonlinear processing clustering techniques—thus providing a detailed cluster label morphology and profile of water gates’ normal operating area. Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%. |
| format | Article |
| id | doaj-art-7d3d4bc56b374940ba165273086da209 |
| institution | DOAJ |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-7d3d4bc56b374940ba165273086da2092025-08-20T02:44:56ZengMDPI AGInfrastructures2412-38112025-02-011023810.3390/infrastructures10020038Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal OperationTheocharis Tsenis0Vassilios Kappatos1Department C: Non-land Transport, Environmental and Economic Issues, Sector of Hellenic Institute of Transportation (HIT), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, GreeceDepartment C: Non-land Transport, Environmental and Economic Issues, Sector of Hellenic Institute of Transportation (HIT), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, GreeceThe detection of acoustic emission events from various failing mechanisms, such as plastic deformations, is a critical element in the monitoring and timely detection of structural failures in infrastructures. This study focuses on the detection of such failures in metal gates at rivers’ lifting dams aiming to increase the reliability of river transport compared to the current situation, thereby, increasing the resilience of transport corridors. During our study, we used lifting dams in both France and Italy where river transport is thriving. A methodology was developed, processing corresponding acoustic emission recordings originating from lifting dams’ metal gates, using advanced denoising—preprocessing, various decompositions, and spectral embeddings associated with various latest nonlinear processing clustering techniques—thus providing a detailed cluster label morphology and profile of water gates’ normal operating area. Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%.https://www.mdpi.com/2412-3811/10/2/38acoustic emissioninfrastructuresupport vector machinevariational auto-encoderspectral clustering |
| spellingShingle | Theocharis Tsenis Vassilios Kappatos Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation Infrastructures acoustic emission infrastructure support vector machine variational auto-encoder spectral clustering |
| title | Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation |
| title_full | Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation |
| title_fullStr | Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation |
| title_full_unstemmed | Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation |
| title_short | Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation |
| title_sort | clustering and vectorizing acoustic emission events of large infrastructures normal operation |
| topic | acoustic emission infrastructure support vector machine variational auto-encoder spectral clustering |
| url | https://www.mdpi.com/2412-3811/10/2/38 |
| work_keys_str_mv | AT theocharistsenis clusteringandvectorizingacousticemissioneventsoflargeinfrastructuresnormaloperation AT vassilioskappatos clusteringandvectorizingacousticemissioneventsoflargeinfrastructuresnormaloperation |