Damage Detection using Enhanced Multivariate Statistical Process Control Technique Conference Paper uri icon

abstract

  • 2016 IEEE. This paper addresses the problem of damage detection technique of structural health monitoring (SHM). Kernel principal components analysis (KPCA)-based generalized likelihood ratio (GLR) technique is developed to enhance the damage detection of SHM processes. The data are collected from the complex three degree of freedom spring-mass-dashpot system in order to calculate the KPCA model. The developed KPCA-based GLR is the method that attempts to combine the advantages of GLR statistic in the cases where process models are not available and a multivariate statistical process control; KPCA. The simulations show the improved performance of the KPCA-based GLR damage detection method.

name of conference

  • 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

published proceedings

  • 2016 17TH INTERNATIONAL CONFERENCE ON SCIENCES AND TECHNIQUES OF AUTOMATIC CONTROL AND COMPUTER ENGINEERING (STA'2016)

author list (cited authors)

  • Chaabane, M., Ben Hamida, A., Mansouri, M., Nounou, H. N., & Avci, O.

citation count

  • 12

complete list of authors

  • Chaabane, Marwa||Ben Hamida, Ahmed||Mansouri, Majdi||Nounou, Hazem N||Avci, Onur

publication date

  • December 2016