Abstract
In this paper, a new Eigencurves method to detect leaks in oil pipelines is presented based on enhanced independent component analysis (EICA) and wavelet transform. EICA is used to derive Eigencurves from appropriately reduced principal component analysis (PCA) space of the training pressure images set. Wavelet transform de-noising (WTDN) is employed to preprocess measured pressure signals before getting the training and test images. In order to detect leaks, a classifier is designed to recognize negative pressure wave curve images by training set. 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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© 2004 Springer-Verlag Berlin Heidelberg
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Zhang, Z., Ye, H., Hu, R. (2004). Application of Enhanced Independent Component Analysis to Leak Detection in Transport Pipelines. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_90
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DOI: https://doi.org/10.1007/978-3-540-28648-6_90
Publisher Name: Springer, Berlin, Heidelberg
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