基于数据特征融合的管网信息物理异常诊断方法
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摘要
随着管网物理空间和信息网络的深度融合,系统面临着物理和信息空间异常带来的运行风险.本文根据管网系统数据量大、耦合性强的特点,提出一种基于数据特征融合的信息物理异常诊断方法.首先通过站场信息数据构建信息增维矩阵并且通过矩阵预分析实现信息传输中断异常的判断.然后基于不同站场信息构建的信息增维协方差矩阵,通过矩阵特征值分布的变化情况对物理异常以及信息传输错误异常进行区分.在此基础上,为了对管网物理异常分类实现系统运行状态的有效分析,将管网信息增维协方差矩阵最大特征向量映射的二维图像作为输入,采用卷积神经网络进行研究,进而实现对物理异常的准确判断.最后通过某实际管网数据进行仿真分析,验证所提方法的有效性.
With deep fusion between pipeline physical network and cyber network, the system is facing operational risks caused by cyber-physical anomalies. According to pipe network features of big data and strong coupling, this paper proposes a cyber-physical abnormity diagnosis method using data feature fusion for pipeline network. Firstly, a cyber augmented matrix can be built by station cyber data, and cyber interrupt is diagnosed by matrix pre-analysis.Furthermore, the cyber augmented covariance matrix is established in light of the different station cyber, and physical anomaly and cyber transmission error anomaly are distinguished from each other by the variation condition of the matrix eigenvalue distribution. To effectively analyze the operating state of the physical network anomaly classification, the two-dimensional images which are mapped by the maximum eigenvectors of the cyber augmented covariance matrix of the pipeline network are regarded as the input signals, meanwhile, a convolutional neural network is utilized to carry out the analysis, thus the accurate judgment of the physical anomaly is realized. Eventually, the effectiveness of the proposed method is demonstrated through the time-domain simulation result obtained on a practical pipeline network.
With deep fusion between pipeline physical network and cyber network, the system is facing operational risks caused by cyber-physical anomalies. According to pipe network features of big data and strong coupling, this paper proposes a cyber-physical abnormity diagnosis method using data feature fusion for pipeline network. Firstly, a cyber augmented matrix can be built by station cyber data, and cyber interrupt is diagnosed by matrix pre-analysis.Furthermore, the cyber augmented covariance matrix is established in light of the different station cyber, and physical anomaly and cyber transmission error anomaly are distinguished from each other by the variation condition of the matrix eigenvalue distribution. To effectively analyze the operating state of the physical network anomaly classification, the two-dimensional images which are mapped by the maximum eigenvectors of the cyber augmented covariance matrix of the pipeline network are regarded as the input signals, meanwhile, a convolutional neural network is utilized to carry out the analysis, thus the accurate judgment of the physical anomaly is realized. Eventually, the effectiveness of the proposed method is demonstrated through the time-domain simulation result obtained on a practical pipeline network.
引文
1 Wang Zhong-Jie,Xie Lu-Lu.Cyber-physical systems:a survey.Acta Automatica Sinica,2011,37(10):1157-1166(王中杰,谢璐璐.信息物理融合系统研究综述.自动化学报,2011,37(10):1157-1166)
2 Wen Jing-Rong,Wu Mu-Qing,Su Jing-Fang.Cyberphysical system.Acta Automatica Sinica,2012,38(4):507-517(温景容,武穆清,宿景芳.信息物理融合系统.自动化学报,2012,38(4):507-517)
3 Park K J,Zheng R,Liu X.Cyber-physical systems:milestones and research challenges.Computer Communications,2012,36(1):1-7
4 Li Jian,Chen Shi-Li,Huang Xin-Jing,Zeng Zhou-Mo,Jin Shi-Jiu.Review of leakage monitoring and quasi real-time detection technologies for long gas&oil pipelines.Chinese Journal of Scientific Instrument,2016,37(8):1747-1760(李健,陈世利,黄新敬,曾周末,靳世久.长输油气管道泄漏监测与准实时检测技术综述.仪器仪表学报,2016,37(8):1747-1760)
5 Liu Jin-Hai,Feng Jian.Research on leak fault intelligent detection method for fluid pipeline based on fuzzy classification.Chinese Journal of Scientific Instrument,2011,32(1):26-32(刘金海,冯健.基于模糊分类的流体管道泄漏故障智能检测方法研究.仪器仪表学报,2011,32(1):26-32)
6 Liu Wei,Liu Hong-Zhao.Pipeline leak detection and location method based on structural similarity criteria.Journal of Central South University(Science and Technology),2017,48(1):134-140(刘炜,刘宏昭.基于结构相似度的管道泄漏检测定位法.中南大学学报(自然科学版),2017,48(1):134-140)
7 Liu Jin-Hai,Zang Dong,Wang Gang.Leakage detection and location method of oil and gas pipelines based on Markov features.Chinese Journal of Scientific Instrument,2017,38(4):944-951(刘金海,臧东,汪刚.基于Markov特征的油气管道泄漏检测与定位方法.仪器仪表学报,2017,38(4):944-951)
8 Kan Zhe,Lang Xian-Ming,Wang Xiao-Lei.Leakage location of branch pipeline based on cyber-physical system architecture.Information and Control,2018,47(1):22-28(阚哲,郎宪明,王晓蕾.基于信息物理系统架构分支管道泄漏定位.信息与控制,2018,47(1):22-28)
9 Wang Wei-Ning,Wang Li,Zhao Ming-Quan,Cai Cheng-Jia,Shi Ting-Ting,Xu Xiang-Min.Image aesthetic classification using parallel deep convolutional neural networks.Acta Automatica Sinica,2016,42(6):904-914(王伟凝,王励,赵明权,蔡成加,师婷婷,徐向民.基于并行深度卷积神经网络的图像美感分类.自动化学报,2016,42(6):904-914)
10 Chang Liang,Deng Xiao-Ming,Zhou Ming-Quan,Wu Zhong-Ke,Yuan Ye,Yang Shuo,et al.Convolutional neural networks in image understanding.Acta Automatica Sinica,2016,42(9):1300-1312(常亮,邓小明,周明全,武仲科,袁野,杨硕,等.图像理解中的卷积神经网络.自动化学报,2016,42(9):1300-1312)
11 Li Yong,Lin Xiao-Zhu,Jiang Meng-Ying.Facial expression recognition with cross-connect LeNet-5 network.Acta Automatica Sinica,2018,44(1):176-182(李勇,林小竹,蒋梦莹.基于跨连接LeNet-5网络的面部表情识别.自动化学报,2018,44(1):176-182)
12 Kang J,Park Y J,Lee J,Wang S H,Eom D S.Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems.IEEE Transactions on Industrial Electronics,2018,65(5):4279-4289
13 Feng J,Li F M,Lu S X,Liu J H,Ma D Z.Injurious or noninjurious defect identification from MFL images in pipeline inspection using convolutional neural network.IEEE Transactions on Instrumentation and Measurement,2017,66(7):1883-1892
14 Lu S X,Feng J,Zhang H G,Liu J H,Wu Z N.An estimation method of defect size from MFL image using visual transformation convolutional neural network.IEEE Transactions on Industrial Informatics,DOI:10.1109/TII.2018.2828811
15 Yang Li-Jian,Cao Hui.Deep learning based weld and flange identification in pipeline.Chinese Journal of Scientific Instrument,2018,39(2):193-202(杨理践,曹辉.基于深度学习的管道焊缝法兰组件识别方法.仪器仪表学报,2018,39(2):193-202)
16 Han Chun-Yu,Huang Chun,Chen Fei,Nan Bing.Surge protection case for Dongying-Linyi parallel oil pipeline.Oil&Gas Storage and Transportation,2008,27(2):53-55(韩春宇,黄春,陈飞,南兵.东临复线水击保护实例分析.油气储运,2008,27(2):53-55)
17 Deng Zhong-Hua,You Dong-Qing,Guo Ye,Li Hong-Jun,Li Yue,Wen Feng,et al.Interruption protection of communication system in Shikong-Lanzhou Crude Oil Pipeline.Oil&Gas Storage and Transportation,2017,36(5):543-547(邓忠华,尤冬青,郭晔,李洪军,李岳,闻峰,等.石兰原油管道通信系统中断运行保护.油气储运,2017,36(5):543-547)
18 He Zhao-Yang,Shang Yi,He Li-Ping,Li Chun,Yin Su-Na.Reasons and solutions of SCADA communication interruption in Mohe-Daqing Crude Oil Pipeline.Oil&Gas Storage and Transportation,2014,33(5):501-504(何兆洋,尚义,何丽萍,黎春,殷素娜.漠大原油管道SCADA通讯中断原因及应对措施.油气储运,2014,33(5):501-504)
19 Bai Z D,Silverstein J W.Spectral Analysis of Large Dimensional Random Matrices(Second Edition).New York:Springer-Verlag,2010.
20 Lecun Y,Bottou L,Bengio Y,Haffner P.Gradient-based learning applied to document recognition.Proceedings of the IEEE,1998,86(11):2278-2324
21 Zhou Fei-Yan,Jin Lin-Peng,Dong Jun.Review of convolutional neural network.Chinese Journal of Computers,2017,40(6):1229-1251(周飞燕,金林鹏,董军.卷积神经网络研究综述.计算机学报,2017,40(6):1229-1251)
2 Wen Jing-Rong,Wu Mu-Qing,Su Jing-Fang.Cyberphysical system.Acta Automatica Sinica,2012,38(4):507-517(温景容,武穆清,宿景芳.信息物理融合系统.自动化学报,2012,38(4):507-517)
3 Park K J,Zheng R,Liu X.Cyber-physical systems:milestones and research challenges.Computer Communications,2012,36(1):1-7
4 Li Jian,Chen Shi-Li,Huang Xin-Jing,Zeng Zhou-Mo,Jin Shi-Jiu.Review of leakage monitoring and quasi real-time detection technologies for long gas&oil pipelines.Chinese Journal of Scientific Instrument,2016,37(8):1747-1760(李健,陈世利,黄新敬,曾周末,靳世久.长输油气管道泄漏监测与准实时检测技术综述.仪器仪表学报,2016,37(8):1747-1760)
5 Liu Jin-Hai,Feng Jian.Research on leak fault intelligent detection method for fluid pipeline based on fuzzy classification.Chinese Journal of Scientific Instrument,2011,32(1):26-32(刘金海,冯健.基于模糊分类的流体管道泄漏故障智能检测方法研究.仪器仪表学报,2011,32(1):26-32)
6 Liu Wei,Liu Hong-Zhao.Pipeline leak detection and location method based on structural similarity criteria.Journal of Central South University(Science and Technology),2017,48(1):134-140(刘炜,刘宏昭.基于结构相似度的管道泄漏检测定位法.中南大学学报(自然科学版),2017,48(1):134-140)
7 Liu Jin-Hai,Zang Dong,Wang Gang.Leakage detection and location method of oil and gas pipelines based on Markov features.Chinese Journal of Scientific Instrument,2017,38(4):944-951(刘金海,臧东,汪刚.基于Markov特征的油气管道泄漏检测与定位方法.仪器仪表学报,2017,38(4):944-951)
8 Kan Zhe,Lang Xian-Ming,Wang Xiao-Lei.Leakage location of branch pipeline based on cyber-physical system architecture.Information and Control,2018,47(1):22-28(阚哲,郎宪明,王晓蕾.基于信息物理系统架构分支管道泄漏定位.信息与控制,2018,47(1):22-28)
9 Wang Wei-Ning,Wang Li,Zhao Ming-Quan,Cai Cheng-Jia,Shi Ting-Ting,Xu Xiang-Min.Image aesthetic classification using parallel deep convolutional neural networks.Acta Automatica Sinica,2016,42(6):904-914(王伟凝,王励,赵明权,蔡成加,师婷婷,徐向民.基于并行深度卷积神经网络的图像美感分类.自动化学报,2016,42(6):904-914)
10 Chang Liang,Deng Xiao-Ming,Zhou Ming-Quan,Wu Zhong-Ke,Yuan Ye,Yang Shuo,et al.Convolutional neural networks in image understanding.Acta Automatica Sinica,2016,42(9):1300-1312(常亮,邓小明,周明全,武仲科,袁野,杨硕,等.图像理解中的卷积神经网络.自动化学报,2016,42(9):1300-1312)
11 Li Yong,Lin Xiao-Zhu,Jiang Meng-Ying.Facial expression recognition with cross-connect LeNet-5 network.Acta Automatica Sinica,2018,44(1):176-182(李勇,林小竹,蒋梦莹.基于跨连接LeNet-5网络的面部表情识别.自动化学报,2018,44(1):176-182)
12 Kang J,Park Y J,Lee J,Wang S H,Eom D S.Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems.IEEE Transactions on Industrial Electronics,2018,65(5):4279-4289
13 Feng J,Li F M,Lu S X,Liu J H,Ma D Z.Injurious or noninjurious defect identification from MFL images in pipeline inspection using convolutional neural network.IEEE Transactions on Instrumentation and Measurement,2017,66(7):1883-1892
14 Lu S X,Feng J,Zhang H G,Liu J H,Wu Z N.An estimation method of defect size from MFL image using visual transformation convolutional neural network.IEEE Transactions on Industrial Informatics,DOI:10.1109/TII.2018.2828811
15 Yang Li-Jian,Cao Hui.Deep learning based weld and flange identification in pipeline.Chinese Journal of Scientific Instrument,2018,39(2):193-202(杨理践,曹辉.基于深度学习的管道焊缝法兰组件识别方法.仪器仪表学报,2018,39(2):193-202)
16 Han Chun-Yu,Huang Chun,Chen Fei,Nan Bing.Surge protection case for Dongying-Linyi parallel oil pipeline.Oil&Gas Storage and Transportation,2008,27(2):53-55(韩春宇,黄春,陈飞,南兵.东临复线水击保护实例分析.油气储运,2008,27(2):53-55)
17 Deng Zhong-Hua,You Dong-Qing,Guo Ye,Li Hong-Jun,Li Yue,Wen Feng,et al.Interruption protection of communication system in Shikong-Lanzhou Crude Oil Pipeline.Oil&Gas Storage and Transportation,2017,36(5):543-547(邓忠华,尤冬青,郭晔,李洪军,李岳,闻峰,等.石兰原油管道通信系统中断运行保护.油气储运,2017,36(5):543-547)
18 He Zhao-Yang,Shang Yi,He Li-Ping,Li Chun,Yin Su-Na.Reasons and solutions of SCADA communication interruption in Mohe-Daqing Crude Oil Pipeline.Oil&Gas Storage and Transportation,2014,33(5):501-504(何兆洋,尚义,何丽萍,黎春,殷素娜.漠大原油管道SCADA通讯中断原因及应对措施.油气储运,2014,33(5):501-504)
19 Bai Z D,Silverstein J W.Spectral Analysis of Large Dimensional Random Matrices(Second Edition).New York:Springer-Verlag,2010.
20 Lecun Y,Bottou L,Bengio Y,Haffner P.Gradient-based learning applied to document recognition.Proceedings of the IEEE,1998,86(11):2278-2324
21 Zhou Fei-Yan,Jin Lin-Peng,Dong Jun.Review of convolutional neural network.Chinese Journal of Computers,2017,40(6):1229-1251(周飞燕,金林鹏,董军.卷积神经网络研究综述.计算机学报,2017,40(6):1229-1251)