Leak Detection in Transport Pipelines Using Enhanced Independent Component Analysis and Support Vector Machines

• Published in: Advances in Natural Computation pp. 95 - 100
• Main Authors: Zhang, Zhengwei, Ye, Hao, Wang, Guizeng, Yang, Jie
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Blind Source Separation; Computer science; control theory; systems; Exact sciences and technology; Independent Component Analysis; Leak Detection; Statistical Learning Theory; Source separation; Statistical analysis; Pipeline processor; Independent component analysis; Leak; Pattern recognition; Blind separation; False alarm rate; Supervised learning; Classification; Vector support machine; Feature extraction; Probability learning; Learning algorithm; Artificial intelligence; Pattern extraction; Principal component analysis
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_16

Independent component analysis (ICA) is a feature extraction technique for blind source separation. Enhanced independent component analysis (EICA), which has enhanced generalization performance, operates in a reduced principal component analysis (PCA) space. SVM is a powerful supervised learning algorithm, which is rooted in statistical learning theory. SVM has demonstrated high generalization capabilities in many pattern recognition problems. In this paper, we integrate EICA with SVM and apply this new method to the leak detection problem in oil pipelines. In features extraction, EICA produces EICA features of the original pressure images. In classification, SVM classified the EICA features as leak or non-leak. The test results based on real data indicate that the method can detect many leak faults from a pressure curve, and reduce the ratio of false and missing alarm than conventional methods.