Print Email Facebook Twitter A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring Title A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring Author Oncescu, Andreea Maria (University of Oxford) Cicirello, A. (TU Delft Engineering Structures; TU Delft Mechanics and Physics of Structures) Contributor Rizzo, Piervincenzo (editor) Milazzo, Alberto (editor) Department Engineering Structures Date 2023 Abstract A self-supervised classification algorithm is proposed for detecting and isolating sensor faults of health monitoring devices. This is achieved by automatically extracting information from failure investigations. This approach uses (i) failure reports for extracting comprehensive failure labels; (ii) recorded data of a faulty monitoring device and the information of the failure type for selecting fault-sensitive features. The features-label pairs are then used to train a classification algorithm, so that when a new set of measurements becomes available, the algorithm is capable of identifying with a high accuracy one of the possible failure types included in the training data set. The proposed approach is successfully applied to the failure investigations conducted on a low-cost wearable device, displaying similar challenges encountered in SHM. Subject Monitoring device failureNatural language processingSelf-supervised machine learningSensor failuresSHM To reference this document use: http://resolver.tudelft.nl/uuid:79e0ac50-fab4-4db4-af3f-cca1d280bbd3 DOI https://doi.org/10.1007/978-3-031-07254-3_57 Publisher Springer Embargo date 2022-12-19 ISBN 9783031072536 Source European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1 Event 10th European Workshop on Structural Health Monitoring, EWSHM 2022, 2022-07-04 → 2022-07-07, Palermo, Italy Series Lecture Notes in Civil Engineering, 2366-2557, 253 LNCE Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2023 Andreea Maria Oncescu, A. Cicirello Files PDF 978_3_031_07254_3_57.pdf 510.82 KB Close viewer /islandora/object/uuid:79e0ac50-fab4-4db4-af3f-cca1d280bbd3/datastream/OBJ/view