Volume 16, Issue 9 (2016)                   Modares Mechanical Engineering 2016, 16(9): 107-112 | Back to browse issues page

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Zadkarami M, Shahbazian M, Salahshoor K. Oil pipeline leak diagnosis using wavelet transform and statistical features with artificial neural network application. Modares Mechanical Engineering. 2016; 16 (9) :107-112
URL: http://journals.modares.ac.ir/article-15-6988-en.html
1- Petroleum University of Technology
2- Petroleum university of technology
Abstract:   (2520 Views)
Oil pipeline leakages, if not properly treated, can result in huge losses. The first step in tackling these leakages is to diagnose their location. This paper employs a data-driven Fault Detection and Isolation (FDI) system not only to detect the occurrence and location of a leakage fault, but also to estimate its severity (size) with extreme accuracy. In the present study, the Golkhari-Binak pipeline, located in southern Iran, is modeled in the OLGA software. The data used to train the data-driven FDI system is acquired by this model. Different leakage scenarios are applied to the pipeline model; then, the corresponding inlet pressure and outlet flow rates are recorded as the training data. The time-domain data are transformed into the wavelet domain; then, the statistical features of the data are extracted from both the wavelet and the time domains. Each of these features are then fed into a Multi-Layer Perceptron Neural Network (MLPNN) which functions as the FDI system. The results show that the system with the wavelet-based statistical features outperforms that of the time-domain based features. The proposed FDI system is also able to diagnose the leakage location and severity with a low False Alarm Rate (FAR) and a high Correct Classification Rate (CCR).
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Article Type: Research Article | Subject: Automation
Received: 2016/07/2 | Accepted: 2016/07/27 | Published: 2016/09/11

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