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Machine learning-based recognition for recognizing operating conditions of multi-product pipelines
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Miao LI1, Lingbo LI1, Zhiheng ZUO1, Li ZHANG2, Luxin JIANG3, Huai SU4, **
China Safety Science Journal | 2024, 34(6) : 127 - 135
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China Safety Science Journal | 2024, 34(6): 127-135
Safety engineering technology
Machine learning-based recognition for recognizing operating conditions of multi-product pipelines
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Miao LI1, Lingbo LI1, Zhiheng ZUO1, Li ZHANG2, Luxin JIANG3, Huai SU4, **
Affiliations
  • 1 South China Company,China Oil & Gas Pipeline Network Corporation,Guangzhou Guangdong 510623,China
  • 2 Smart Gas & Pipeline Division,Kunlun Digital Technology Co.,Ltd.,Beijing 102206,China
  • 3 PipeChina Science and Technology Institute,China Oil & Gas Pipeline Network Corporation,Langfang Hebei 065000,China
  • 4 National Engineering Laboratory for Pipeline Safety,Beijing Key Laboratory of Urban Oil and Gas Distribution Technology,China University of Petroleum,Beijing 102249,China
Published: 2024-06-28 doi: 10.16265/j.cnki.issn1003-3033.2024.06.1410
Outline
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In order to solve the problems that some operating conditions could not be automatically identified and the accuracy of abnormal operating condition recognition was low in the process of monitoring the production and operation of multi-product pipeline system,the intelligent operating condition recognition method was applied to construct a multi-product pipeline operating condition recognition model with real-time monitoring capability. First,logic rule discrimination methods and event logs in the multi-product pipeline system were used to supplement the data labels. Second,the data were segmented according to the start and end time of the operating conditions,and the subsequence of different operating conditions were extracted by using the sliding window. Third,the features of subsequence were extracted to construct the model for operating condition recognition of multi-product pipelines,and the recognition effects of six classification models,namely,random forest (RF),adaptive boosting (AdaBoost),support vector machine (SVM),time series forest (TSF),random interval spectral forest (RISF) and sequence learner (SEQL),were compared and analyzed. Finally,a real multi-product pipeline was used as an example for model validation. The results show that the TSF model has the highest recognition accuracy for the four operating conditions of valve switching,valve internal leakage,pigging and sling pump,and is more suitable for the recognition of short-term operating conditions. In contrast,the recognition precision of the AdaBoost model has a higher probability of including the true value in the 95% confidence interval.

machine learning  /  multi-product pipeline  /  operating conditions  /  operating condition recognition  /  classification model
Miao LI, Lingbo LI, Zhiheng ZUO, Li ZHANG, Luxin JIANG, Huai SU. Machine learning-based recognition for recognizing operating conditions of multi-product pipelines[J]. China Safety Science Journal, 2024 , 34 (6) : 127 -135 . DOI: 10.16265/j.cnki.issn1003-3033.2024.06.1410
Year 2024 volume 34 Issue 6
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.06.1410
  • Receive Date:2023-12-20
  • Online Date:2025-07-09
  • Published:2024-06-28
Article Data
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History
  • Received:2023-12-20
  • Revised:2024-03-21
Funding
Affiliations
    1 South China Company,China Oil & Gas Pipeline Network Corporation,Guangzhou Guangdong 510623,China
    2 Smart Gas & Pipeline Division,Kunlun Digital Technology Co.,Ltd.,Beijing 102206,China
    3 PipeChina Science and Technology Institute,China Oil & Gas Pipeline Network Corporation,Langfang Hebei 065000,China
    4 National Engineering Laboratory for Pipeline Safety,Beijing Key Laboratory of Urban Oil and Gas Distribution Technology,China University of Petroleum,Beijing 102249,China
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表12种不同金属材料的力学参数

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Number of
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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