Damage detection in structural health monitoring using kernel PLS based GLR

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The objective of this paper is to extend the applicability of the GLR method to a wide range of practical systems. Most real systems are nonlinear, multivariate, and are best represented by input-output type of models. Kernel partial least squares (KPLS) models have been widely used to represent such systems. Therefore, in this paper, kernel PLS-based GLR method will be utilized in practice to improve damage detection in Structural Health Monitoring (SHM). The developed kernel PLS-based GLR technique combines the benefits of the multivariate input-output kernel PLS model and the statistical fault detection GLR statistic which showed performance in the cases where process models are not available. GLR is a well-known statistical detection method that relies on maximizing the detection probability for a given false alarm rate. To calculate the kernel PLS model, we use the data collected from the complex 3DOF spring-mass-dashpot system. The simulation results show improved performance of kernel PLS-based GLR in damage detection compared to the classical kernel PLS method.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538605516
DOIs
Publication statusPublished - 19 Oct 2017
Event3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017 - Fez, Morocco
Duration: 22 May 201724 May 2017

Other

Other3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017
CountryMorocco
CityFez
Period22/5/1724/5/17

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Keywords

  • Damage Detection
  • Generalized Likelihood Ratio
  • Kernel Partial Least Squares
  • Structural Health Monitoring

ASJC Scopus subject areas

  • Signal Processing

Cite this

Chaabane, M., Mansouri, M., Nounou, H., Nounou, M., Ben Slima, M., & Ben Hamida, A. (2017). Damage detection in structural health monitoring using kernel PLS based GLR. In Proceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017 [8075555] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ATSIP.2017.8075555