Journal
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
Volume 43, Issue -, Pages 479-487Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jlp.2016.06.018
Keywords
Pipeline leakage; Fault detection and isolation (FDI) system; Multi-layer perceptron neural network; (MLPNN) classifier; Wavelet transform; Statistical features
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Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved. (C) 2016 Elsevier Ltd. All rights reserved.
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