Susceptibility on the strain field change as function of the coupling between the effect produced by damage appearance and the change in the load conditions

When strain sensors are used in order to gather valuable information about structural integrity, the main idea is to compare patterns in the strain field for the pristine conditions and possible damaged conditions. However, any change in the strain field caused by other conditions different from damage occurrence must be isolated from the analysis. In previous works the authors have demonstrated than even when the changes in the local strain field caused by a defect are very small and may go faded easily, it is possible to detect such small changes by using appropriate robust automated techniques. The authors have focused their attention in methodologies based on Principal Component Analysis (PCA) and some nonlinear extensions such as Hierarchical Non- linear PCA (h-NLPCA) and the development of several unfolding and scaling techniques, which allows dealing with multiple load conditions. However, when the load conditions are very different and promotes big changes in the strain field, it is necessary to isolate such load conditions in order to uncoupling the effect of the damage occurrence and the effect of the severe change in load conditions. By means of automatic clustering techniques based on Self Organizing Maps (SOM), an Optimal Baseline Selection (OBS) technique was developed for damage detection based on strain measurements and strain field pattern recognition.