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RISS 인기검색어
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- 저자 : Nordin Jamaludin (검색결과 2 건)
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Zakiah Abd Halim,Nordin Jamaludin,Syarif Junaidi,Syed Yusainee Syed Yahya 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.4
Current steel tubes inspection techniques are invasive, and the interpretation and evaluation of inspection results are manually done byskilled personnel. This paper presents a statistical analysis of high frequency stress wave signals captured from a newly developed noninvasive,non-destructive tube inspection technique known as the vibration impact acoustic emission (VIAE) technique. Acoustic emission(AE) signals have been introduced into the ASTM A179 seamless steel tubes using an impact hammer, and the AE wave propagationwas captured using an AE sensor. Specifically, a healthy steel tube as the reference tube and four steel tubes with through-hole artificialdefect at different locations were used in this study. The AE features extracted from the captured signals are rise time, peak amplitude,duration and count. The VIAE technique also analysed the AE signals using statistical features such as root mean square (r.m.s.), energy,and crest factor. It was evident that duration, count, r.m.s., energy and crest factor could be used to automatically identify the presence ofdefect in carbon steel tubes using AE signals captured using the non-invasive VIAE technique.
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Detection of tube defect using the autoregressive algorithm
Zakiah A. Hali,Nordin Jamaludin,Syarif Junaidi,Syed Yusainee Syed Yahya 국제구조공학회 2015 Steel and Composite Structures, An International J Vol.19 No.1
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.
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