Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Partially Supervised Learning for Data-Driven Structural Health Monitoring
The cost of labelling data by engineer inspections remains a significant issue for performance and health monitoring. In many cases, this is because the actual data annotation process is expensive (e.g. non-destructive testing) or it is simply infeasible to label all the measurements (e.g. lack of access). Often, however, it is possible to provide a small number of budget-restricted labels, to describe the measurements. In these scenarios, methods for partially supervised learning are proposed. Active learning, semi-supervised learning, and transfer learning are summarised here—demonstrated with simulated monitoring examples. Each family of algorithms is shown to significantly improve conventional methods for data-driven monitoring.
Partially Supervised Learning for Data-Driven Structural Health Monitoring
The cost of labelling data by engineer inspections remains a significant issue for performance and health monitoring. In many cases, this is because the actual data annotation process is expensive (e.g. non-destructive testing) or it is simply infeasible to label all the measurements (e.g. lack of access). Often, however, it is possible to provide a small number of budget-restricted labels, to describe the measurements. In these scenarios, methods for partially supervised learning are proposed. Active learning, semi-supervised learning, and transfer learning are summarised here—demonstrated with simulated monitoring examples. Each family of algorithms is shown to significantly improve conventional methods for data-driven monitoring.
Partially Supervised Learning for Data-Driven Structural Health Monitoring
Structural Integrity
Cury, Alexandre (Herausgeber:in) / Ribeiro, Diogo (Herausgeber:in) / Ubertini, Filippo (Herausgeber:in) / Todd, Michael D. (Herausgeber:in) / Bull, Lawrence A. (Autor:in) / Hughes, A. J. (Autor:in) / Rogers, T. J. (Autor:in) / Gardner, Paul (Autor:in) / Worden, Keith (Autor:in) / Dervilis, Nikolaos (Autor:in)
Structural Health Monitoring Based on Data Science Techniques ; Kapitel: 19 ; 389-411
Structural Integrity ; 21
24.10.2021
23 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Partially supervised learning , Active learning , Semi-supervised learning , Transfer learning , Structural health monitoring , Prognostics and health management , Condition monitoring Computer Science , Data Structures and Information Theory , Artificial Intelligence , Machine Learning , Statistics, general , Engineering
| British Library Conference Proceedings | 2013