A PCA/Natural Frequency-Based Approach for Damage Detection: Implementation on a Laboratory Structure Subjected to Environmental Variability

In recent years, modal parameter-based techniques have gained particular interest in the development of structural health monitoring (SHM) strategies. Natural frequencies are the most commonly used dynamic parameter for damage detection in vibration-based SHM as damages manifest as a change in structural properties (stiffness or mass). Unfortunately, environmental changes, such as temperature drift, can cause modal frequency variations even more predominant than possible incoming damages. In this chapter, a laboratory truss girder made with steel beams and aluminum rods subjected to environmental variability has been analyzed. Modal parameters and ambient conditions have been evaluated through the employment of accelerometers and temperature transducers. Natural frequencies and ambient temperature have been processed to develop damage detection techniques with robust immunity to environmental interference. A principal component analysis (PCA)-based approach has been performed to identify the features most sensitive to temperature changes. Once the environmentally sensitive features were excluded, the remaining features containing information about the health of the structure were processed to obtain the baseline of the system in healthy conditions. Then predetermined damages, such as mass variation or loss of bolt preload at rod intersections, have been induced in the structure. Condition-sensitive features of the system have been used to develop damage identification techniques through statistical indicators (i.e., Mahalanobis squared distance). The results prove that a damage index (DI) evaluated based on these statistical indicators characterizes the incoming damage in the examined structure without the influence of environmental variations.