Techno Press
Techno Press

Steel and Composite Structures   Volume 19, Number 1, July 2015, pages 131-152
DOI: http://dx.doi.org/10.12989/scs.2015.19.1.131
 
Detection of tube defect using the autoregressive algorithm
Zakiah A. Halim, Nordin Jamaludin, Syarif Junaidi and Syed Yusainee

 
Abstract     [Full Text]
    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.
 
Key Words
    autoregressive; defect identification; impact excitation; pattern recognition; stress wave
 
Address
(1) Zakiah A. Halim:
Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia;
(2) Nordin Jamaludin, Syarif Junaidi:
Department of Mechanical Engineering & Materials, Faculty of Engineering and Built, Universiti Kebangsaan Malaysia, 43000 UKM, Bangi, Selangor, Malaysia;
(3) Syed Yusainee:
Faculty of Applied Science, Universiti Teknologi MARA, 42300 Shah Alam, Selangor, Malaysia.
 

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