Structural health monitoring of high-speed railways using ultrasonic guided waves

Yanfeng Shen, Junfang Wang, Yiqing Ni

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper presents a damage detection strategy for high-speed railways using piezoelectric active sensors. Multimodal ultrasonic guided waves generated by a piezoelectric transmitter propagate along the rail track, undergo dispersion, interact with the damage zone, and are finally picked up by the sensors. First, numerical investigations are carried out to understand the guided wave features and their interaction mechanism with typical damage scenarios in the railways. The modal analysis of a finite element scheme with Bloch-Floquet condition is conducted to obtain the dispersion characteristics and the mode shapes of the rail track guided waves. Optimum wave generation location and frequency were explored using a small-size local coupled field finite element model. Further, a Local Interaction Simulation Approach (LISA) model was developed to achieve efficient simulation of elastic wave propagation in railway structures. The LISA procedure was coded using the Compute Unified Device Architecture (CUDA), which enables the highly parallelized computing on powerful Graphics Processing Units (GPUs). This transient dynamic analysis reveals the influence of rail track features and damage signature on the sensing signals. Finally, full-scale experiments on a BS 90A rail track with embedded piezoelectric sensors are carried out to compare with the numerical investigations. This study shows that the active sensing system possess promising potential for the in-situ health monitoring of railway structures.
Original languageEnglish
Title of host publicationStructural Health Monitoring 2017
Subtitle of host publicationReal-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
PublisherDEStech Publications
Pages2927-2934
Number of pages8
Volume2
ISBN (Electronic)9781605953304
Publication statusPublished - 1 Jan 2017
Event11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 - Stanford University, Stanford, United States
Duration: 12 Sep 201714 Sep 2017

Conference

Conference11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
Country/TerritoryUnited States
CityStanford
Period12/09/1714/09/17

ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications

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