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Information Journal Paper

Title

OIL PIPELINE LEAK DIAGNOSIS USING WAVELET TRANSFORM AND STATISTICAL FEATURES WITH ARTIFICIAL NEURAL NETWORK APPLICATION

Pages

  107-112

Abstract

 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 is 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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  • Cite

    APA: Copy

    ZADKARAMI, MORTEZA, SHAHBAZIAN, MEHDI, & SALAHSHOOR, KARIM. (2016). OIL PIPELINE LEAK DIAGNOSIS USING WAVELET TRANSFORM AND STATISTICAL FEATURES WITH ARTIFICIAL NEURAL NETWORK APPLICATION. MODARES MECHANICAL ENGINEERING, 16(9), 107-112. SID. https://sid.ir/paper/179771/en

    Vancouver: Copy

    ZADKARAMI MORTEZA, SHAHBAZIAN MEHDI, SALAHSHOOR KARIM. OIL PIPELINE LEAK DIAGNOSIS USING WAVELET TRANSFORM AND STATISTICAL FEATURES WITH ARTIFICIAL NEURAL NETWORK APPLICATION. MODARES MECHANICAL ENGINEERING[Internet]. 2016;16(9):107-112. Available from: https://sid.ir/paper/179771/en

    IEEE: Copy

    MORTEZA ZADKARAMI, MEHDI SHAHBAZIAN, and KARIM SALAHSHOOR, “OIL PIPELINE LEAK DIAGNOSIS USING WAVELET TRANSFORM AND STATISTICAL FEATURES WITH ARTIFICIAL NEURAL NETWORK APPLICATION,” MODARES MECHANICAL ENGINEERING, vol. 16, no. 9, pp. 107–112, 2016, [Online]. Available: https://sid.ir/paper/179771/en

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