面向机动性输油管线的AI驱动预测性维护与健康管理系统

An AI-driven Predictive Maintenance and Health Management System for Rapidly Deployable Oil Pipelines

摘要

针对机动性输油管线部署场景复杂、工况动态多变，以及现有预测性维护与健康管理方法存在小样本预测精度不足、泛化能力弱、运维成本偏高等问题，开展机理−数据混合驱动的智能预测方法研究。构建适配机动部署场景的边缘−云协同系统架构，提出融合腐蚀机理约束与动态偏置长短期记忆网络的混合驱动预测算法，通过混合损失函数引入物理先验，缓解小样本场景下的模型泛化瓶颈。基于某野外机动输油管线30个月实测数据开展验证，结果表明，该模型预测均方根误差为0.012 ± 0.001，平均绝对误差为0.009 ± 0.001，较纯机理模型与单一长短期记忆网络模型预测精度分别提升85.9%与62.5%；不同训练样本占比下精度衰减幅度显著低于同类方法，复杂部署环境下预测准确率保持90%以上，整体运维成本降低40%以上。该研究可为机动输油管线智能化运维提供技术路径，也可为同类临时部署工业设施健康管理提供参考。

Abstract

Given the complex deployment scenarios and dynamic operating conditions of rapidly deployable fuel pipelines, existing predictive maintenance and health management approaches suffer from limitations such as insufficient prediction accuracy under small-sample conditions, weak generalization capabilities, and high operational costs. To address these issues, this study investigates a mechanism-data hybrid-driven intelligent prediction method. We develop an edge-cloud collaborative system architecture tailored for rapid deployment scenarios and propose a hybrid prediction algorithm integrating corrosion mechanism constraints with dynamic bias long-term memory networks. By incorporating physical prior knowledge through a hybrid loss function, we alleviate the model generalization bottleneck in small-sample scenarios. Validation is conducted using 30 months of real-world measurement data collected from a rapidly deployable fuel pipeline. Results demonstrate that the proposed model achieves a root mean square error (RMSE) of 0.012 ± 0.001 and a mean absolute error (MAE) of 0.009 ± 0.001, improving prediction accuracy by 85.9% and 62.5% over pure mechanism-based models and long-term-memory network models, respectively. Furthermore, the proposed model exhibits significantly lower accuracy degradation under varying training dataset proportions than comparable methods, maintaining over 90% prediction accuracy in complex deployment environments while reducing overall operational costs by more than 40%. This study provides a technical framework for intelligent operation and maintenance of rapidly deployable fuel pipelines and offers valuable insights for health management of similar temporary industrial facilities.

关键词

机动性输油管线 / 预测性维护与健康管理 / 混合驱动算法 / 边缘−云协同 / 动态偏置LSTM

Key words

rapidly deployable fuel pipeline / predictive maintenance and health management / hybrid-driven algorithm / edge-cloud collaboration / dynamic bias LSTM

参考文献

[1] 闫乐, 张靖. 油气管道与输油站一体化安全运维机制研究[J]. 中国化工贸易, 2026, 18(9): 160-162.

[2] Yan Le, Zhang Jing. Research on integrated safety operation and maintenance mechanism of oil pipeline and oil transmission station[J]. China Chemical Trade, 2026, 18(9): 160-162.

[3] 张伟. 管道泄漏智能检测技术的应用与可靠性分析[C/OL]// 新质生产力驱动第二产业发展与招标采购创新论坛——实践路径探索与案例分享专题论文集(第二册). 北京: 世纪中文出版社, 2026. (2026-04-27). https://doi.org/10.26914/c.cnkihy.2026.008931.

[4] 王磊, 黄余, 孙杰, 等 . 油气管道泄漏诊断技术应用现状分析与未来展望[J]. 石油和化工设备, 2026, 29(3): 29-33.

[5] Wang Lei, Huang Yu, Sun Jie, et al. Analysis of current status and future outlook of leakage diagnosis technology for oil and gas pipelines[J]. Petro & Chemical Equipment, 2026, 29(3): 29-33.

[6] 韩一萱, 施先亮, 张炜健, 等 . 基于AI预测性维护的供应链技术投入与融资策略研究[J/OL]. 中国管理科学, 1-25 (2026-04-30). https://doi.org/10.16381/j.cnki.issn1003-207x.2025.1921.

[7] Han Yixuan, Shi Xianliang, Zhang Weijian, et al. Research on technology investment and financing strategy of supply chain based on AI predictive maintenance[J/OL]. Chinese Journal of Management Science, 1-25 (2026-04-30). https://doi.org/10.16381/j.cnki.issn1003-207x.2025.1921.

[8] 赵岩龙, 岳雷舒, 郑天慧, 等 . 人工智能在有杆采油系统生产动态预测中的应用与展望[J]. 石油机械, 2026, 54(4): 1-12.

[9] Zhao Yanlong, Yue Leishu, Zheng Tianhui, et al. Artificial intelligence in production performance prediction for rod pumping systems: application and prospects[J]. China Petroleum Machinery, 2026, 54(4): 1-12.

[10] 胡青松, 李飞, 单露露, 等 . 石油输送管道微小缺陷智能检测方法[J]. 西安石油大学学报(自然科学版), 2025, 40(4): 134-142.

[11] Hu Qingsong, Li Fei, Shan Lulu, et al. Intelligent detection method for small defects in oil pipeline[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2025, 40(4): 134-142.

[12] 赵学良, 韩丽君, 韩嘉航. 人工智能赋能工业化学的挑战、路径与未来范式[J/OL]. 化工进展, 1-14 (2026-05-26). https://doi.org/10.16085/j.issn.1000-6613.2026-0525.

[13] Zhao Xueliang, Han Lijun, Han Jiahang, et al. Navigating challenges, forging, pathways: transformative future of artifical intelligence in industrial chemistry[J/OL]. Chemical Industry and Engineering Progress, 1-14 (2026-05-26). https://doi.org/10.16085/j.issn.1000-6613.2026-0525.

[14] 汪征, 王金江, 孙雪皓, 等 . 基于安全屏障的油气生产系统智能运维进展及展望[J]. 天然气工业, 2025, 45(8): 129-141.

[15] Wang Zheng, Wang Jinjiang, Sun Xuehao, et al. Research progress and prospect of safety barrier-based intelligent operation and maintenance for oil and gas production system[J]. Natural Gas Industry, 2025, 45(8): 129-141.

[16] 孙铁良, 郭祎, 王珩宇, 等 . 输油管道调控运行智能化探索与实践[J]. 油气储运, 2026, 45(2): 229-239.

[17] Sun Tieliang, Guo Yi, Wang Hengyu, et al. Exploration and practice of intelligent control and operation of oil pipelines[J]. Oil & Gas Storage and Transportation, 2026, 45(2): 229-239.

[18] 黎义斌, 井卫民, 赵文举, 等 . 泵联网的基础架构设计与应用[J]. 排灌机械工程学报, 2026, 44(5): 466-478.

[19] Li Yibin, Jing Weimin, Zhao Wenju, et al. Infrastructure design and application of pump IoT[J]. Journal of Drainage and Irrigation Machinery Engineering, 2026, 44(5): 466-478.

[20] 钱文振, 马晓宇, 李春奇, 等 . 天然气管道失效事故统计与多因素耦合机制[J]. 油气储运, 2026, 45(4): 451-466.

[21] Qian Wenzhen, Ma Xiaoyu, Li Chunqi, et al. Natural gas pipeline failure accidents: statistics and multi-factor coupling mechanism[J]. Oil & Gas Storage and Transportation, 2026, 45(4): 451-466.

[22] 廖绮, 刘春颖, 杜渐, 等 . 人工智能赋能油气管道智慧运行的应用及展望[J/OL]. 油气储运, 1-15(2024-03-13). https://link.cnki.net/urlid/13.1093.TE.20240313.1045.004.

[23] 孙铁良, 沈亮, 吕慕昊, 等 . 油气管网智能调控路径规划与落地实践[J]. 油气储运, 2025, 44(12): 1321-1334.

[24] Sun Tieliang, Shen Liang, Lyu Muhao, et al. Path planning and implementation practice for intelligent regulation of oil and gas pipeline networks[J]. Oil & Gas Storage and Transportation, 2025, 44(12): 1321-1334.

[25] 张弢甲, 张海峰, 燕冰川, 等 . 基于光纤传感的管道清管检测器智能定位与跟踪[J]. 石油机械, 2025, 53(11): 28-34.

[26] Zhang Taojia, Zhang Haifeng, Yan Bingchuan, et al. Intelligent positioning and tracking of pipeline pig detector based on fiber-optic sensing[J]. China Petroleum Machinery, 2025, 53(11): 28-34.

[27] 张沙沙, 马业辉, 王炳钦. 非可视环境下智能腐蚀检测实验系统设计[J]. 实验室研究与探索, 2025, 44(10): 1-7.

[28] Zhang Shasha, Ma Yehui, Wang Bingqin. Design and realization of intelligent corrosion detection experimental system in non-visible environment based on Internet of Things technology[J]. Research and Exploration in Laboratory, 2025, 44(10): 1-7.

[29] 马祥, 张斌. 铁路无人机自主智能巡检平台技术框架与实践[J/OL]. 铁道科学与工程学报, 1-13 (2026-05-14). https://doi.org/10.19713/j.cnki.43-1423/u.T20260218.

[30] Ma Xiang, Zhang Bin. UAV-based autonomous intelligent inspection for railways: technical architecture and practical applications[J/OL]. Journal of Railway Science and Engineering, 1-13 (2026-05-14). https://doi.org/10.19713/j.cnki.43-1423/u.T20260218.

[31] 王展祥, 万骞. 以“智”稳“制”: 人工智能稳制造业比重的内在机理与政策启示[J]. 东岳论丛, 2026, 47(4): 99-112.

[32] Wang Zhanxiang, Wan Qian. Stabilizing the manufacturing sector's share with artificial intelligence: internal mechanisms and policy implications[J]. Dongyue Tribune, 2026, 47(4): 99-112.

[33] 梅闯. 机电仪一体化技术在石油化工设备中的创新应用[J]. 石化技术, 2026, 33(3): 115-117.