Probabilistic multiple model neural network based leak detection system: Experimental study |
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| Affiliation: | 1. Masdar Institute for Science & Technology, United Arab Emirates;2. Non-linearity and Complexity Research Group, Aston University, UK;1. School of Information Science and Engineering, Yanshan University, 438, Hebei Avenue, QinHuangDao, Hebei Province 066004, PR China;2. Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004, Hebei Province, PR China;3. China Petroleum and Gas Pipeline Telecommunication and Electricity Engineering Corporation, Langfang 065000, Hebei Province, PR China;1. College of Pipeline and Civil Engineering in China University of Petroleum (East China), Qingdao 266580, China;2. Sinopec Qingdao LNG Co.,Ltd, Qingdao 266400, China;3. Key Laboratory of Qingdao Oil and Gas Storage and Transportation Technologies, China;4. Key Laboratory of CNPC Heavy Gas Transportation and LNG Technologies, China;1. School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China;2. School of Chemical Engineering and Energy, Zhengzhou University, Zhengzhou, China;1. CINVESTAV IPN Unit Guadalajara, 45019 Zapopan, Jalisco, Mexico;2. Control Systems Department, GIPSA-lab, Grenoble INP, Saint Martin d''Hères, France;3. Institut Universitaire de France;1. College of Mechanical and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China;2. Chengde Petroleum College, Hebei 067000, China |
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| Abstract: | This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results. |
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| Keywords: | Multiple models Pipeline leak detection Uncertainty Paired t-test Negative pressure wave |
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