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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%.<\/jats:p>","DOI":"10.1177\/0142331220928145","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T07:55:32Z","timestamp":1591689332000},"page":"2708-2718","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Data driven leakage diagnosis for oil pipelines: An integrated approach of factor analysis and deep neural network classifier"],"prefix":"10.1177","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1070-5947","authenticated-orcid":false,"given":"Morteza","family":"Zadkarami","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Shiraz University, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Akbar","family":"Safavi","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Shiraz University, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4888-5883","authenticated-orcid":false,"given":"Mohammad","family":"Taheri","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Shiraz University, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabienne Fariba","family":"Salimi","sequence":"additional","affiliation":[{"name":"ADEPP Academy, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"key":"bibr1-0142331220928145","doi-asserted-by":"publisher","DOI":"10.2118\/177459-MS"},{"key":"bibr2-0142331220928145","unstructured":"Agarap AF (2018) Deep learning using rectified linear units (relu).\n                      arXiv preprint arXiv:1803.08375\n                      ."},{"key":"bibr3-0142331220928145","volume-title":"Pipeline Variable Uncertainties and Their Effects Leak Detectability","author":"API 1149","year":"2015","edition":"2"},{"key":"bibr4-0142331220928145","volume-title":"Subsea Pipeline Integrity and Risk Management","author":"Bai Y","year":"2014"},{"key":"bibr5-0142331220928145","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop CM","year":"2006"},{"key":"bibr6-0142331220928145","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(03)00433-8"},{"key":"bibr7-0142331220928145","doi-asserted-by":"publisher","DOI":"10.1016\/j.jlp.2016.03.010"},{"key":"bibr8-0142331220928145","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106581"},{"key":"bibr9-0142331220928145","volume-title":"Pattern Classification","author":"Duda RO","year":"2012"},{"key":"bibr10-0142331220928145","doi-asserted-by":"publisher","DOI":"10.1080\/15567030903058618"},{"key":"bibr11-0142331220928145","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.02.030"},{"key":"bibr12-0142331220928145","volume-title":"American School of Gas Measurement Technology (ASGMT)","author":"Fiedler J","year":"2014"},{"key":"bibr13-0142331220928145","doi-asserted-by":"publisher","DOI":"10.1016\/j.bej.2018.04.015"},{"key":"bibr14-0142331220928145","doi-asserted-by":"publisher","DOI":"10.1007\/BF03187449"},{"key":"bibr15-0142331220928145","unstructured":"Konersmann R, K\u00fchl C, Ludwig J (2009) On the risks of transporting liquid and gaseous fuels in pipelines. 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