Abstract—The conventional stress wave signal interpretation
in heat exchanger tube inspection is human dependent. The
difficulties associated with accurate defect interpretations are
skills and experiences of the inspector. Hence, in present study,
alternative pattern recognition approach was proposed to
interpret the presence of defect in carbon steel heat exchanger
tubes SA179. Several high frequency stress wave signals
propagated in the tubes due to impact are captured using
Acoustic Emission method. In particular, one reference tube
and two defective tubes were adopted. The signals were then
clustered using the feature extraction algorithms. This paper
tested two feature extraction algorithms namely Principal
Component Analysis (PCA) and Auto-Regressive (AR). The
pattern recognition results showed that the AR algorithm is
more effective in defect identification. Good comparisons with
the commonly global statistical analysis demonstrate the
effective application of the present approach for defect
detection.
Index Terms—Auto-regressive, pattern recognition,
principal component analysis, stress wave.
A. H. Zakiah is with the Universiti Teknikal Malaysia Melaka, 76100
Melaka, Malaysia (e-mail: zakiahh@utem.edu.my).
N. Jamaludin and J. Syarif are with the Universiti Kebangsaan Malaysia,
43600 Bangi, Malaysia (e-mail: nordin@eng.ukm.my, syarif@eng.ukm.my).
S. Y. S. Yahya is with the Universiti Teknologi MARA, 40450 Shah
Alam, Malaysia (e-mail: syedy237@salam.uitm.edu.my).
[PDF]
Cite: Zakiah A. Halim, Nordin Jamaludin, Syarif Junaidi, and Syed Yusainee Syed Yahya, "Pattern Recognition Approach of Stress Wave Propagation in Carbon Steel Tubes for Defect Detection," International Journal of Computer Theory and Engineering vol. 7, no. 2, pp. 139-144, 2015.
