eprintid: 22352 rev_number: 24 eprint_status: archive userid: 1903 dir: disk0/00/02/23/52 datestamp: 2014-04-07 15:38:54 lastmod: 2016-04-21 14:15:08 status_changed: 2014-04-07 15:38:54 type: article metadata_visibility: show item_issues_count: 0 creators_name: Güemes Gordo, Jesús Alfredo creators_name: Sierra Pérez, Julian creators_name: Rodellar, J. creators_name: Mujica, Luis Eduardo title: A robust procedure for damage detection from strain measurements based on principal component analysis publisher: Trans Tech Publications rights: by-nc-nd ispublished: pub subjects: aeronautica full_text_status: public keywords: Principal component Analysis, fiber optic sensors. abstract: FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie. date_type: published date: 2013 publication: Key Engineering Materials volume: 558 pagerange: 128-138 id_number: 10.4028/www.scientific.net/KEM.558.128 institution: Aeronauticos department: Materiales2 refereed: TRUE issn: 1013-9826 official_url: http://www.scientific.net/KEM.558.128 referencetext: [1] Kourti, T, and John MacGregor. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemometrics and intelligent laboratory systems, (1995) 3-21. [2] Nomikos, Paul, and Jhon F. MacGregor. Monitoring batch Processes Using Multiway Principal Component Analysis. AIChE Journal, (1994) 1361-1375. [3] Westerhuis, Johan A, Theodora Kourti, and Jhon F MacGregor. Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, (1999) 397-413. [4] Gurden, S, J Westerhuis, R Bro, and A Smilde. A comparision of multiway regression and scaling methods. Chemometrics and Intellingent Laboratory Systems. 59 (2001) 121-136. [5] Villez, K, K Steppe, and D De Pauw. Use of unfold PCA for on-line plant stress monitoring and sensor failure detection. Biosystems Engineering, (2009) 23-34. [6] Mujica, L, D Tibaduiza, and J Rodellar. Data driven multiactuator piezoelectric system for structural damage localization. Fifth world conference on structural control and monitoring (2010). [7] Wold, S, N Kettaneh, H Friden, and A Holmberg. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and intelligent laboratory systems. (1998) 331-340. [8] Jackson, E, and G Mudholkar. Control procedures for residual associates with PCA. Technometrics, (1979) 341-349. [9] Burgos, D.A, L Mujica, A Güemes, and J Rodellar. Active piezoelectric system using PCA. Fifth European Workshop on Structural Health Monitoring. (2010) 164-169. [10] Mujica, L.E., J. Rodellar, A. Fernandez, and A. Guemes. Q-statistic and T2-statistic PCAbased measures for damage assessment in structures. Structural Health Monitoring.(2010) 1-15. [11] García, Carlos E. Caracterización de coeficientes de Expansion termica. Informe Técnico, (2010). [12] Sierra, J, and A Güemes. Detección de daño en materiales compuestos mediante fibra óptica. Actas del IX congreso nacional de materiales compuestos. Girona: AEMAC, 2011. citation: Güemes Gordo, Jesús Alfredo and Sierra Pérez, Julian and Rodellar, J. and Mujica, Luis Eduardo (2013). A robust procedure for damage detection from strain measurements based on principal component analysis. "Key Engineering Materials", v. 558 ; pp. 128-138. ISSN 1013-9826. https://doi.org/10.4028/www.scientific.net/KEM.558.128 . document_url: http://oa.upm.es/22352/1/INVE_MEM_2013_151239.pdf