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Sensing Technologies for Damage Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 3055

Special Issue Editor


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Guest Editor
NRC Institute for Information Technology, National Research Council Canada, Ottawa, ON K1A 0R6, Canada
Interests: machine learning; signal processing; time series analysis; damage detection

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to highlight the most recent research regarding sensing technologies for damage detection. This Special Issue focuses on the development and challenge of sensing technologies for damage detection. The broader aim is to collect high-quality papers from researchers around the world working in this area to make damage detection more widespread and more effective using sensing technologies. Research articles and reviews are solicited that provide a comprehensive insight into the sensing technologies for damage detection on any aspect of novel sensor development and applications. Topics of interest include but are not limited to the following:

  • Structural health monitoring
  • Nondestructive test and evaluation
  • Novel structural health monitoring modeling and evaluation
  • Advanced computational methods and signal processing
  • Big data collection and processing for the SHM
  • Material assessment using SHM systems
  • Structural management using monitoring systems
  • Advanced sensors technologies and reliability
  • Embedded sensing system for structural health monitoring
  • Big data processing and management for SHM
  • New damage detection, localization, or quantification methods
  • Computer-vision-based damage detection methods
  • Advanced deep machine-learning-based damage detection
  • Application of SHM to large-scale infrastructures
  • UAV-based infrastructure monitoring

Dr. Julio J. Valdes
Guest Editor

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Published Papers (1 paper)

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Research

23 pages, 16909 KiB  
Article
Crack Detection of Threaded Steel Rods Based on Ultrasonic Guided Waves
by Kunhong Peng, Yi Zhang, Xian Xu, Jinsong Han and Yaozhi Luo
Sensors 2022, 22(18), 6885; https://doi.org/10.3390/s22186885 - 12 Sep 2022
Cited by 2 | Viewed by 2611
Abstract
Fatigue cracks are typical damage of threaded steel rods under dynamic loads. This paper presents a study on ultrasonic guided waves-based, fatigue-crack detection of threaded rods. A threaded rod with given sizes is theoretically simplified as a cylindrical rod. The propagation characteristics of [...] Read more.
Fatigue cracks are typical damage of threaded steel rods under dynamic loads. This paper presents a study on ultrasonic guided waves-based, fatigue-crack detection of threaded rods. A threaded rod with given sizes is theoretically simplified as a cylindrical rod. The propagation characteristics of ultrasonic guided waves in the cylindrical rod are investigated by semi-analytical finite element method and the longitudinal L(0, 1) modal ultrasonic guided waves in low frequency band is proposed for damage detection of the rod. Numerical simulation on the propagation of the proposed ultrasonic guided waves in the threaded rod without damage shows that the thread causes echoes of the ultrasonic guided waves. A numerical study on the propagation of the proposed ultrasonic guided waves in the threaded rod with a crack on the intersection of the smooth segment and the threaded segment shows that both linear indexes (Rf and ARS) and nonlinear indexes (βre and β) are able to detect the crack. A constant-amplitude tensile fatigue experiment was conducted on a specimen of the threaded rod to generate fatigue cracks in the specimen. After every 20,000 loading cycles, the specimen was tested by the proposed ultrasonic guided waves and evaluated by the linear indexes and nonlinear indexes. Experimental results show that both the linear and nonlinear indexes of the ultrasonic guided waves are able to identify the crack before it enters the rapid growth stage and the nonlinear indexes detect the crack easier than the linear indexes. Full article
(This article belongs to the Special Issue Sensing Technologies for Damage Detection)
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