Monitoring of Structural Systems Using Improved Data Driven Damage Detection Technique Academic Article

abstract

• 2018 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.

authors

• Nounou, Hazem
• Nounou, Mohamed

published proceedings

• IFAC PAPERSONLINE

author list (cited authors)

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

citation count

• 0

complete list of authors

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

publication date

• January 2018

publisher

• Elsevier  Publisher

keywords

• Damage Detection
• Exponentially Weighted Moving Average
• Generalized Likelihood Ratio Test
• Kernel Partial Least Squares
• Structural Health Monitoring

Digital Object Identifier (DOI)

• 10.1016/j.ifacol.2018.09.673

start page

• 843

end page

• 848

volume

• 51

issue

• 24

URL

• http://dx.doi.org/10.1016/j.ifacol.2018.09.673