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Diagnosing the changes of structural behaviors using monitoring data is animportant objective of structural health monitoring (SHM). The changes instructural behaviors are usually manifested as the feature changes in monitoredstructural responses; thus, developing effective methods for automaticallydetecting such changes is of considerable significance. Existing methods forchange detection in SHM are mainly used for scalar or vector data, thusincapable of detecting the changes of the features represented by complex data,e.g., the probability density functions (PDFs). Detecting the abrupt changesoccurred in the distributions (represented by PDFs) associated with thedamage-sensitive features extracted from SHM data are usually of crucialinterest for structural condition assessment; however, the SHM community stilllacks effective diagnostic tools for detecting such changes. In this study, achange-point detection method is developed in the functional data-analyticframework for PDF-valued sequence, and it is leveraged to diagnose thedistributional information break encountered in structural conditionassessment. A major challenge in PDF-valued data modeling or analysis is thatthe PDFs are special functional data subjecting to nonlinear constraints. Totackle this issue, the PDFs are embedded into the Bayes space, and theassociated change-point model is constructed by using the linear structure ofthe Bayes space; then, a hypothesis testing procedure is presented fordistributional change-point detection based on the isomorphic mapping betweenthe Bayes space and a functional linear space. Comprehensive simulation studiesare conducted to validate the effectiveness of the proposed method as well asdemonstrate its superiority over the competing method. Finally, an applicationto real SHM data illustrates its practical utility in structural conditionassessment.

structural health monitoring

A change-point detection method for detecting and locating the abrupt  changes in distributions of damage-sensitive features of SHM data, with  application to structural condition assessment