A temperature-driven one-class support vector machine method for anomaly detection

Yanjie Zhu, Irwanda Laory, Yi Qing Ni

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Competent data-driven anomaly detection methods should catch the meaningful changes in measurements due to the structural abnormity. However, the distinct thermal effect may produce significant temperature-related fluctuations in the strain measurements when compared with the reflections due to the real structural damages. This paper presents a temperature-driven one-class support vector machine method, designated as Td-OCSVM, for anomaly detection, which introduces the idea of blind source separation (BSS) for thermal feature extraction and further employs OCSVM for anomaly detection. First of all, the temperature-related strain variations can be investigated and revealed by employing independent component analysis based on the criteria of maximization nongaussianity, which is one of the most popular solutions for BSS problem. Afterwards, the OCSVM is adopted for anomaly detection on those separated temperature-induced responses. In case study, the Ricciolo curved viaduct in Switzerland is utilized for the purpose of evaluating the proposed method. This viaduct had been monitored in both construction and in-service period. The data obtaied during the construction period is labelled as anomalous condition, which is expected to be identified by Td-OCSVM. The performance of Td-OCSVM is also compared with OCSVM only with temperature-driven process. The interpretation results can demonstrate the outperformance of Td-OCSVM with the higher detectable ability when compared with OCSVM.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2019
Subtitle of host publicationEnabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
EditorsFu-Kuo Chang, Alfredo Guemes, Fotis Kopsaftopoulos
PublisherDEStech Publications Inc.
Pages3243-3250
Number of pages8
ISBN (Electronic)9781605956015
Publication statusPublished - 1 Jan 2019
Event12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019 - Stanford, United States
Duration: 10 Sep 201912 Sep 2019

Publication series

NameStructural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
Volume2

Conference

Conference12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019
CountryUnited States
CityStanford
Period10/09/1912/09/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Information Management

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