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.
|Title of host publication||European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1|
|Editors||Piervincenzo Rizzo, Alberto Milazzo|
|Number of pages||11|
|Publication status||Published - 2023|
|Event||10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy|
Duration: 4 Jul 2022 → 7 Jul 2022
|Name||Lecture Notes in Civil Engineering|
|Conference||10th European Workshop on Structural Health Monitoring, EWSHM 2022|
|Period||4/07/22 → 7/07/22|
Bibliographical noteGreen 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.
- Monitoring device failure
- Natural language processing
- Self-supervised machine learning
- Sensor failures