Steel and Composite Structures

  • 제19권1호
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  • Pages.131-152
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  • 2015
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Detection of tube defect using the autoregressive algorithm

  • Halim, Zakiah A. (Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka) ;
  • Jamaludin, Nordin (Department of Mechanical Engineering & Materials, Faculty of Engineering and Built, Universiti Kebangsaan Malaysia) ;
  • Junaidi, Syarif (Department of Mechanical Engineering & Materials, Faculty of Engineering and Built, Universiti Kebangsaan Malaysia) ;
  • Yusainee, Syed (Faculty of Applied Science, Universiti Teknologi MARA)
  • 투고 : 2014.03.27
  • 심사 : 2014.12.29
  • 발행 : 2015.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

Easy detection and evaluation of defect in the tube structure is a continuous problem and remains a significant demand in tube inspection technologies. This study is aimed to automate defect detection using the pattern recognition approach based on the classification of high frequency stress wave signals. The stress wave signals from vibrational impact excitation on several tube conditions were captured to identify the defect in ASTM A179 seamless steel tubes. The variation in stress wave propagation was captured by a high frequency sensor. Stress wave signals from four tubes with artificial defects of different depths and one reference tube were classified using the autoregressive (AR) algorithm. The results were demonstrated using a dendrogram. The preliminary research revealed the natural arrangement of stress wave signals were grouped into two clusters. The stress wave signals from the healthy tube were grouped together in one cluster and the signals from the defective tubes were classified in another cluster. This approach was effective in separating different stress wave signals and allowed quicker and easier defect identification and interpretation in steel tubes.

키워드

참고문헌

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피인용 문헌

  1. Influence of Defects on Reinforced Concrete Fracture Performance in Improved Wedge Splitting Tests vol.47, pp.2, 2018, https://doi.org/10.1520/JTE20170176