Convolutional neural networks for real-time and wireless damage detection
المؤلف | Avci O. |
المؤلف | Abdeljaber O. |
المؤلف | Kiranyaz, Mustafa Serkan |
المؤلف | Inman D. |
تاريخ الإتاحة | 2022-04-26T12:31:21Z |
تاريخ النشر | 2020 |
اسم المنشور | Conference Proceedings of the Society for Experimental Mechanics Series |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1007/978-3-030-12115-0_17 |
الملخص | Structural damage detection methods available for structural health monitoring applications are based on data preprocessing, feature extraction, and feature classification. The feature classification task requires considerable computational power which makes the utilization of centralized techniques relatively infeasible for wireless sensor networks. In this paper, the authors present a novel Wireless Sensor Network (WSN) based on One Dimensional Convolutional Neural Networks (1D CNNs) for real-time and wireless structural health monitoring (SHM). In this method, each CNN is assigned to its local sensor data only and a corresponding 1D CNN is trained for each sensor unit without any synchronization or data transmission. This results in a decentralized system for structural damage detection under ambient environment. The performance of this method is tested and validated on a steel grid laboratory structure. |
اللغة | en |
الناشر | Springer New York LLC |
الموضوع | Classification (of information) Convolution Damage detection Dynamics Feature extraction Neural networks One dimensional Structural analysis Structural dynamics Structural health monitoring Ambient environment Computational power Convolutional neural network Decentralized system Feature classification Real time Structural damage detection Wireless structural health monitoring Wireless sensor networks |
النوع | Conference Paper |
الصفحات | 129-136 |
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