Failure Detection on Train Bogie Frames Using Guided Waves and an Image-Based Approach

Abstract

Welded tubular steel structures (WTSS) are widely used in bogie frames of train, which usually play vital roles in the whole framework. Structural Health Monitoring (SHM) in terms of ultrasonic guided waves (UGWs)-based technology on such kind of structures deserves in-depth investigation. In this study, propagation behavior of UGWs in a WTSS, which is a real size model for a train bogie frame segment, was investigated using both finite element method (FEM) and experimental analysis for the purpose of damage evaluation. A sensor network made up of piezoelectric wafers was employed to generate UGWs and collect response signals. Excitations at chosen frequencies were applied to the active PZT transducers with the aim of getting enough signals for study. An imaging approach based on a signal feature named “time of maximal difference” (ToMD) and the subsequent concept “damage presence probability” (DPP) was developed and then utilized to estimate the presences and locations of multiple welding damages in welding zones. In virtual of a dual-level data fusion procedure, visualized and numerical results for damage evaluation were obtained and analyzed. It’s indicated that the recommended approach is effective and accurate in evaluating damage presence and location in the given conditions. Subsequently, two fatigue cracks, as common failures in WTSS, were introduced into a steel angle structure (SAS) which is half of the WTSS and a steel plate respectively for fatigue crack identification and monitoring. Similar approach was used for identifying the fatigue crack. Results show that fatigue cracks can reflect guided waves by reason of discontinuous contact of crack surfaces, and demonstrate the feasibility and effectiveness of the proposed method for real-time monitoring of fatigue cracks in bogie frames of the train.