Abstract
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.
Supported by the National Natural Science Fund of China (60274015) and the 863 Program of China.
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Zhang, Z., Ye, H., Wang, G., Yang, J. (2005). Leak Detection in Transport Pipelines Using Enhanced Independent Component Analysis and Support Vector Machines. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_16
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DOI: https://doi.org/10.1007/11539117_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
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