Monitoring of Structural Systems Using Improved Data Driven Damage Detection Technique

Marwa Chaabane, Majdi Mansouri, Ahmed Ben Hamida, Hazem Nounou, Mohamed Nounou

Research output: Contribution to journalArticle

Abstract

The objective of this paper is to propose a new damage detection technique based on multiscale kernel partial least squares (MSKPLS), optimized exponentially weighted moving average (OEWMA) and generalized likelihood ratio test (GLRT) in order to enhance monitoring of structural systems. The developed technique attempts to combine the advantages of the EWMA and GLRT charts with those of multiscale nonlinear input-output model (kernel PLS) and multi-objective optimization. The performance of the developed damage detection technique is assessed using two illustrative examples, synthetic data and simulated International Association for Structural Control-American society of Civil engineers (IASC-ASCE) benchmark structure.

Original languageEnglish
Pages (from-to)843-848
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number24
DOIs
Publication statusPublished - 1 Jan 2018

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Damage detection
Monitoring
Multiobjective optimization
Engineers

Keywords

  • Damage Detection
  • exponentially weighted moving average
  • generalized likelihood ratio test
  • kernel Partial Least Squares
  • structural health monitoring

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Monitoring of Structural Systems Using Improved Data Driven Damage Detection Technique . / Chaabane, Marwa; Mansouri, Majdi; Hamida, Ahmed Ben; Nounou, Hazem; Nounou, Mohamed.

In: IFAC-PapersOnLine, Vol. 51, No. 24, 01.01.2018, p. 843-848.

Research output: Contribution to journalArticle

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