Damage detection using enhanced multivariate statistical process control technique

Marwa Chaabane; Ahmed Ben Hamida; Majdi Mansouri; Hazem N. Nounou; Onur Avci

doi:10.1109/STA.2016.7952052

Damage detection using enhanced multivariate statistical process control technique

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	234-238
Number of pages	5
ISBN (Electronic)	9781509034079
DOIs	https://doi.org/10.1109/STA.2016.7952052
Publication status	Published - 16 Jun 2017
Externally published	Yes
Event	17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Sousse, Tunisia Duration: 19 Dec 2016 → 21 Dec 2016

Publication series

Name	2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings

Conference

Conference	17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016
Country/Territory	Tunisia
City	Sousse
Period	19/12/16 → 21/12/16

Keywords

Damage detection
GLR
Kernel PCA
SHM

Access to Document

10.1109/STA.2016.7952052

Cite this

Chaabane, M., Ben Hamida, A., Mansouri, M., Nounou, H. N., & Avci, O. (2017). Damage detection using enhanced multivariate statistical process control technique. In 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings (pp. 234-238). Article 7952052 (2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/STA.2016.7952052

Chaabane, Marwa ; Ben Hamida, Ahmed ; Mansouri, Majdi et al. / Damage detection using enhanced multivariate statistical process control technique. 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 234-238 (2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings).

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title = "Damage detection using enhanced multivariate statistical process control technique",

abstract = "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.",

keywords = "Damage detection, GLR, Kernel PCA, SHM",

author = "Marwa Chaabane and {Ben Hamida}, Ahmed and Majdi Mansouri and Nounou, {Hazem N.} and Onur Avci",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 ; Conference date: 19-12-2016 Through 21-12-2016",

year = "2017",

month = jun,

day = "16",

doi = "10.1109/STA.2016.7952052",

language = "English",

series = "2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

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Chaabane, M, Ben Hamida, A, Mansouri, M, Nounou, HN & Avci, O 2017, Damage detection using enhanced multivariate statistical process control technique. in 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings., 7952052, 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 234-238, 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016, Sousse, Tunisia, 19/12/16. https://doi.org/10.1109/STA.2016.7952052

Damage detection using enhanced multivariate statistical process control technique. / Chaabane, Marwa; Ben Hamida, Ahmed; Mansouri, Majdi et al.
2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 234-238 7952052 (2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Damage detection using enhanced multivariate statistical process control technique

AU - Chaabane, Marwa

AU - Ben Hamida, Ahmed

AU - Mansouri, Majdi

AU - Nounou, Hazem N.

AU - Avci, Onur

PY - 2017/6/16

Y1 - 2017/6/16

N2 - 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.

AB - 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.

KW - Damage detection

KW - GLR

KW - Kernel PCA

KW - SHM

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U2 - 10.1109/STA.2016.7952052

DO - 10.1109/STA.2016.7952052

M3 - Conference contribution

AN - SCOPUS:85024372871

T3 - 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings

SP - 234

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BT - 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 19 December 2016 through 21 December 2016

ER -

Chaabane M, Ben Hamida A, Mansouri M, Nounou HN, Avci O. Damage detection using enhanced multivariate statistical process control technique. In 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 234-238. 7952052. (2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings). doi: 10.1109/STA.2016.7952052

Damage detection using enhanced multivariate statistical process control technique

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