International Journal of Computer Theory and Engineering

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.