IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v261y2022ipbs0360544222022095.html

Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines

Author

Listed:
  • Wang, Chang
  • Zheng, Jianqin
  • Liang, Yongtu
  • Wang, Bohong
  • Klemeš, Jiří Jaromír
  • Zhu, Zhu
  • Liao, Qi

Abstract

The operation monitoring of multi-product pipelines helps to grasp the operation dynamics, detect abnormal situations in time, and assist on-site operation management. However, due to the complexity of the scheduling plan, the operating conditions of pipelines change frequently, which makes it difficult to accurately recognise condition types. To solve the above problem, an intelligent monitoring framework for operating conditions is proposed to simultaneously achieve the system recognition of steady, unsteady, and abnormal conditions. (i) The proposed monitoring framework extracts temporal and spatial characteristics of condition samples through four modules: Modules 1 and 2 form an unsupervised model for monitoring state changes and capturing temporal characteristics of condition samples; Module 3 is utilised to capture the spatial characteristics; the fusion layer based on Module 4 is applied to nonlinearly fit the spatiotemporal characteristics, and while monitoring the status changes of condition, it can also accurately recognise whether the condition is normal operation adjustment or abnormal condition. (ii) Taking a simulated pipeline and a real pipeline as examples, the effectiveness of the proposed monitoring framework is verified, and the accuracy, precision, recall, and F1 score of the recognition results reach 98.56%, 98.56%, 97.68%, and 98.12%. (iii) Through the sensitivity analysis of each module, accuracy, precision, recall, and F1 score are reduced to 96.10%, 96.10%, 95.83%, and 96.83% (i.e., only 2.46%, 2.46%, 1.85%, 1.29% differences) without Module I, which proves that the framework has strong robustness and generalisation. (iv) Finally, an intelligent analysis and control system of multi-product pipelines is designed for future applications. Consequently, the proposed intelligent monitoring framework can guide the safe operation and management of multi-product pipelines on-site.

Suggested Citation

  • Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222022095
    DOI: 10.1016/j.energy.2022.125325
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222022095
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125325?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    2. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin & Yao, Jun, 2021. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Zhou, Xingyuan & van Gelder, P.H.A.J.M. & Liang, Yongtu & Zhang, Haoran, 2020. "An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Liu, Shengli & Liang, Yongtu, 2021. "Statistics of catastrophic hazardous liquid pipeline accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    5. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Zheng, Jianqin & Dai, Yuanhao & Liang, Yongtu & Liao, Qi & Zhang, Haoran, 2020. "An online real-time estimation tool of leakage parameters for hazardous liquid pipelines," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).
    7. Peng, Rui & Liu, Bin & Zhai, Qingqing & Wang, Wenbin, 2019. "Optimal maintenance strategy for systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 624-632.
    8. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    9. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    10. He, Guoxi & Li, Yansong & Huang, Yuanjie & Sun, Liying & Liao, Kexi, 2019. "A framework of smart pipeline system and its application on multiproduct pipeline leakage handling," Energy, Elsevier, vol. 188(C).
    11. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    12. Xiang, W. & Zhou, W., 2021. "Bayesian network model for predicting probability of third-party damage to underground pipelines and learning model parameters from incomplete datasets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Chang & Zhou, Hua & Lin, Sen & Weng, Xiaodan & Shao, Yu & Yu, Tingchao, 2025. "A hybrid deep survival model for failure modeling of water distribution networks coupling physical survival and data reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    2. Dongyan Fan & Sicen Lai & Hai Sun & Yuqing Yang & Can Yang & Nianyang Fan & Minhui Wang, 2025. "Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir," Energies, MDPI, vol. 18(4), pages 1-25, February.
    3. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Ma, Yunlu & Wang, Bohong & Liao, Qi & Xu, Ning & Ali, Arshid Mahmood & Rashid, Muhammad Imtiaz & Shahzad, Khurram, 2024. "A deep learning-based approach for predicting oil production: A case study in the United States," Energy, Elsevier, vol. 288(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Zhu, Xian-Kui & Herrington, Joshua, 2026. "Comments on “Failure analysis of corroded hydrogen-blended natural gas pipelines based on finite element analysis and genetic algorithm-back propagation neural network†[262 (2025) 111174]," Reliability Engineering and System Safety, Elsevier, vol. 269(C).
    3. Chen, Qian & Yang, Kai & Yan, Jing & Varbanov, Petar Sabev & Wang, Bohong & Friedler, Ferenc & Zuo, Lili & Xing, Xiaokai, 2025. "Short-period supply reliability evaluation of a gas pipeline network based on transient operation optimization and support vector regression," Energy, Elsevier, vol. 331(C).
    4. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    6. Wang, Junkai & Wang, Tianbiao & Ma, Dazhong, 2025. "Pipeformer: A multi-channel patching-based transformer method with pyramid cross-attention for leak detection of energy transportation system," Energy, Elsevier, vol. 335(C).
    7. Ye, Lin & Wang, Chengyou & Zhou, Xiao & Jiang, Baocheng & Yu, Changsong & Qin, Zhiliang, 2025. "Natural gas pipeline weak leakage detection based on negative pressure wave decomposition and feature enhancement," Reliability Engineering and System Safety, Elsevier, vol. 257(PB).
    8. Miao, Xingyuan & Zhao, Hong, 2024. "Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Chen, Zhanfeng & Li, Xuyao & Wang, Wen & Li, Yan & Shi, Lei & Li, Yuxing, 2023. "Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    11. Hu, Sile & Xu, Xiaofeng & Liu, Dunnan & Yang, Ning & Li, Chenxi & Xu, Erfeng & Wu, Fan & Tao, Yao, 2025. "Optimal dispatch strategy for grand base wind-solar-energy storage systems using machine learning and goal programming," Renewable Energy, Elsevier, vol. 253(C).
    12. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).
    13. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
    14. Dongyu Wang & Xiwen Cui & Dongxiao Niu, 2022. "Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    15. Zhang, Fengxia & Shen, Jingyuan & Liao, Haitao & Ma, Yizhong, 2021. "Optimal preventive maintenance policy for a system subject to two-phase imperfect inspections," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    16. Chenyuan Liu & Heng Li & Kexin Li & Yue Wu & Baogang Lv, 2025. "Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review," Energies, MDPI, vol. 18(6), pages 1-20, March.
    17. Mao, Mingxuan & Wang, Lining & Jiang, Sheng & Zhang, Hao, 2026. "Meteorological element prediction for renewable energy systems: Comprehensive comparison on deep learning algorithms with/without hyperparameters tuning," Energy, Elsevier, vol. 346(C).
    18. Zou, Yuanru & Wang, Shunli & Hai, Nan & Blaabjerg, Frede & Fernandez, Carlos & Cao, Wen, 2025. "Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries," Energy, Elsevier, vol. 332(C).
    19. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    20. Tan, Quanwei & Zhu, Jiebei & Xue, Guijun & Xie, Wenju, 2025. "A hybrid heat load forecasting model based on multistage decomposition and dynamic adaptive loss function," Energy, Elsevier, vol. 335(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222022095. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.