Monitoring of rail bolted joint looseness with PZT network-based EMI technique under a deep learning framework

Lu Zhou, Si Xin Chen, Alex Choy, Yi Qing Ni

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

Abstract

Rail bolted joints (RBJs) are vital components served as either fastening rails or connecting adjacent rail sections in rail systems. In the era of high-speed rail (HSR), bolted joint fasteners are the most common types to keep rail tracks in position; although continuously welded rails (CWR) are mainly used in HSR lines for safety and ride comfort concern under high-speed operation, insulated rail joints (IRJ) are still widely utilized in low-speed zones (e.g., railway stations). Loosening of RBJs has potential risk to failure of circuit insulation, track displacement and misalignment, and subsequently even derailment. The monitoring of loosening of RBJs is therefore highly desired to maintain safe operation. The electromechanical impedance (EMI) is a promising ultrasonic structural health monitoring technique for structural deficiency or damage diagnosis. This paper introduces a novel EMI method for real-time monitoring of loosening of RBJs by employing a network of piezoelectric lead zirconate titanate (PZT) transducers pre-implemented on the host structure. The embedded PZT network and the host structure form a coupled system, and the coupled EMI which is highly sensitive to localized structural changes is measured to indicate the loosening of bolted joints. Convolutional neural networks (CNN) are applied to automatically extract key features from the joint EMI data and quantify the preloading forces on the bolted joints. While some damage metrics cannot well reflect the state of the RBJ, the proposed model can effectively and precisely predict the looseness of the bolted joint and outperform the model that utilizes only the data from one sensor.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2019
Subtitle of host publicationEnabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
EditorsFu-Kuo Chang, Alfredo Guemes, Fotis Kopsaftopoulos
PublisherDEStech Publications Inc.
Pages2841-2848
Number of pages8
ISBN (Electronic)9781605956015
Publication statusPublished - 1 Jan 2019
Event12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019 - Stanford, United States
Duration: 10 Sep 201912 Sep 2019

Publication series

NameStructural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
Volume2

Conference

Conference12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019
CountryUnited States
CityStanford
Period10/09/1912/09/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Information Management

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