Data driven leakage diagnosis for oil pipelines: An integrated approach of factor analysis and deep neural network classifier

Abstract

This paper proposes a novel data-based leakage diagnosis method for big datasets, which identifies the leak occurrence, its size, and its location. Different statistical features are used to express the changes in flow and pressure signals at different leakage scenarios. To improve the performances of the leakage diagnosis approach, factor analysis (FA) is employed for dimension reduction purposes. The optimal features of both pressure and flow signals are then fed as input vectors to a deep neural network (DNN) classifier. The proposed leakage diagnosis method has been applied to the first 20 km of the Golkhari-Binak oil pipeline, located in Iran. The leakage isolation accuracy has been compared with some related works. Simulation results show that the proposed method significantly outperforms others with the average correct classification rate (CCR) of about 98%.