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2023 | OriginalPaper | Buchkapitel

A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring

verfasst von : Andreea-Maria Oncescu, Alice Cicirello

Erschienen in: European Workshop on Structural Health Monitoring

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring
verfasst von
Andreea-Maria Oncescu
Alice Cicirello
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-031-07254-3_57