Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles

This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case cover the objectives this study. Compared other detection approaches described literature, proposed is efficiently able detect higher levels detection, namely identifying both location and severity using low-cost structural health monitoring (SHM) system limited number sensors; for example, accelerometers. integration Bayesian inference, stochastic framework, approach, makes it possible utilize benefit data fusion merging informative from multiple features, which increases quality accuracy results. findings provide decision-maker information required manage maintenance, repair, or replacement procedures.