Ultrasonic flaw classification of seafloor petroleum transporting pipeline based on chaotic genetic algorithm and SVM

Article type: Research Article

Authors: Fei, Chunguo | Han, Zhengzhi | Liu, Qingkun

Affiliations: Department of Automation, Intelligent Engineering Laboratory, Shanghai Jiaotong University, China | Institute of Automatic Detection, Shanghai Jiaotong University, China

Note: [] Corresponding author: Chunguo Fei, Class A0303222, Minhang Campus, Shanghai Jiaotong University, No. 800 Dongchuan Road, Shanghai, P.R. China. Tel.: +86 21 62932083 810; Fax: +86 21 62932083 802; E-mail: chunguo@eyou.com

Abstract: The flaw classification has become one of important tasks in evaluating the seafloor petroleum transporting pipeline integrity. With the aim to classify different kinds of artificial flaws from ultrasonic signals, we develop a novel flaw classification system. In this system we employ wavelet packet decomposition (WPD) to extract the features of ultrasonic signals, employ a new optimization algorithm (chaotic genetic algorithm-CGA) to get rid of redundant and irrelevant features, and employ support vector machine (SVM) classifier to classify the flaws. We use a 5 MHz focal wideband transducer to test four kinds of artificial flaws and collect ultrasonic signals at a 40 MHz sampling rate by a high speed A/D card. Noise cancellation is implemented by adaptive filtering. For comparison purpose, we use different methods as feature selector. Then we compare their results. Through experiment, we can conclude that our system can improve the performance of the SVM classifier for flaws in seafloor pipeline significantly.

Keywords: Ultrasonic inspecting, feature selection, chaotic genetic algorithm (CGA), support vector machine (SVM), wavelet packet decomposition (WPD)

Journal: Journal of X-Ray Science and Technology, vol. 14, no. 1, pp. 1-9, 2006