Minimizing Misclassification of Damage using Extreme Value Statistics

Structural health monitoring is a process of evaluating and ensuring the safety and integrity of structural systems by converting sensor data into structural health monitoring information. The first and most important objective of structural health monitoring is to ascertain with confidence if damage is present or not. The idea is to characterize only the normal condition of a structure and the baseline data are used as a reference. When data are measured during continuous monitoring, the new data are compared with the reference. A significant deviation from the reference will signal damage. This decision-making procedure necessitates the establishment of a decision boundary. Choosing the decision boundary is often based on the assumption that the distribution of the data is Gaussian in nature. While the problem of damage detection focuses attention on the outliers or extreme values of the data, i.e. those points in the tails of the distribution, the establishment of the decision boundary based on the normality assumption weighs the central population of the data. This unwarranted assumption about the nature of the data distribution can significantly impair the performance of a structural health monitoring system by increasing false positive and negative indications of damage. In this study, a robust and automated damage classifier is developed by properly modeling the tails of the distribution using extreme value statistics. 1 Assistant Professor 2 Research Associate 3 Professor 4 Technical Staff Member