Principal component analysis and self-organizing maps for damage detection and classification under temperature variations
Document typeConference lecture
Rights accessOpen Access
The use of statistical techniques for data driven has proven very useful in multivariable analysis as a pattern recognition approach. Among their multiple advantages such as data reduction, multivariable analysis and the definition of statistical models built with data from experimental trials, they provide robustness and allow avoiding the need of the development of physical models which sometimes are difficult for modelling especially when the system is complex. In this paper, a methodology based on Principal Component Analysis (PCA) is developed and used for building statistical baseline models comprising the dynamics from the monitored healthystructureunderdifferenttemperatureconditions.Inasecondstep, fortesting the proposed methodology, data from the structure at different structural states and under different temperature conditions are projected into the baseline models in order to obtain statistical measures (Scores and Q-index) which are included as feature vectors in a Self-Organizing Map for the damage detection and classification tasks. The methodology is evaluated using ultrasonic signals collected from an aluminium plate and a stiffened composite panel. Results show that all the simulated states are successfully classified no matter what the kind of damage or the temperature is present in both structures.
CitationAnaya, M., Tibaduiza, D.A., Torres-Arredondo, M.A., Pozo, F. Principal component analysis and self-organizing maps for damage detection and classification under temperature variations. A: International Workshop on Structural Health Monitoring. "Structural Health Monitoring 2015: System Realiability for Verification and Implementation". Palo Alto (Califòrnia): 2015, p. 1220-1227.