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Structural Health Monitoring for Architectural Heritage: Case Studies in Central Italy Read chapter Read first chapter

2023 | OriginalPaper | Chapter

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

Authors : Andreea-Maria Oncescu, Alice Cicirello

Published in: European Workshop on Structural Health Monitoring

Publisher: 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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previous chapter Design Criteria for Structural Health Monitoring Systems. Application to the Construction of Arches Using the Cantilevered Cable-Stayed Technique. Tajo Bridge Experience
next chapter The Application of the SVD-FDD Hybrid Method to Bridge Mode Shape Estimation
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Metadata
Title
A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring
Authors
Andreea-Maria Oncescu
Alice Cicirello
Copyright Year
2023
Book
European Workshop on Structural Health Monitoring

Print ISBN: 978-3-031-07253-6

Electronic ISBN: 978-3-031-07254-3

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-07254-3_57