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

Andreea Maria Oncescu, Alice Cicirello*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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.

Original languageEnglish
Title of host publicationEuropean Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1
EditorsPiervincenzo Rizzo, Alberto Milazzo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages564-574
Number of pages11
ISBN (Print)9783031072536
DOIs
Publication statusPublished - 2023
Event10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy
Duration: 4 Jul 20227 Jul 2022

Publication series

NameLecture Notes in Civil Engineering
Volume253 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference10th European Workshop on Structural Health Monitoring, EWSHM 2022
Country/TerritoryItaly
CityPalermo
Period4/07/227/07/22

Bibliographical note

Accepted Author Manuscript

Keywords

  • Monitoring device failure
  • Natural language processing
  • Self-supervised machine learning
  • Sensor failures
  • SHM

Fingerprint

Dive into the research topics of 'A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring'. Together they form a unique fingerprint.

Cite this