Detection of tube defect using the autoregressive algorithm

Abd Halim, Zakiah and Jamaludin, Nordin and Junaidi, Syarif and Syed Yusainee, Syed Yahya (2015) Detection of tube defect using the autoregressive algorithm. Steel and Composite Structures, 19. pp. 131-152. ISSN 1598-6233

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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.

Item Type: Article
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Mechanical Engineering
Depositing User: Muhammad Afiz Ahmad
Date Deposited: 31 Mar 2017 00:50
Last Modified: 31 Mar 2017 00:50
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