Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles

Abstract

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

Assessment of catenary condition monitoring by means of pantograph head acceleration and Artificial Neural Networks

“…In this work, we considered wear on the contact wire by modifying the contact wire height at which the contact takes place. Thus, zcw = z cw + z wear (2) in which z cw is obtained from Eq. ( 1) and z wear represents the height of the contact wire removed due to wear.…”

“…Monitoring the condition of the whole pantograph-catenary system to avoid the use of these dedicated train units has now become a hot topic in research. In other studies, [2] proposed a strategy for monitoring the position and severity of the damage on the catenary masts based on an eigenfrequency-based Bayesian approach, while mast inclination was also included in [3]. Cantilever health monitoring approaches have also been proposed by detecting their components from images and Deep Convolutional Neural Networks (DCCN) [4,5].…”

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

“…Regression models [28], blind source separation techniques [29], the Mahalanobis squared distance [30,31], principal component analysis (PCA), and factor analysis have been implemented to treat environmental and operational effects [32]. Extensive works on damage identification, modeling approaches, and EOF treatment may be found in [33][34][35][36][37][38][39].…”