基于多通道数据流在线相关分析及聚类的闸站工程安全监测
|
摘要
闸站工程自动安全监测可积累大量高质量监测数据,然而对这些数据的在线自动分析手段较为有限。该文提出一种针对多通道实时监测数据流的在线相关分析与聚类方法,以挖掘多个感兴趣测点通道数据流之间的联系。该方法能够在线快速计算数据流的统计特征,在计算数据流之间相关性度量的基础上,对多数据流进行自动聚类。以泰州高港闸站工程安全监测系统为例,针对扬压力、伸缩缝、温度等多类型共65个通道数据流进行在线相关分析与聚类,一次特征计算、分析与聚类总时长低于1 s,满足在线处理的实时性要求。该文提出的方法能够判断闸站工程渗压情况、伸缩缝与温度变化特性等,可有效发现潜在的工程安全问题或传感器故障。
Sluice-pump station projects usually consist of many widely distributed hydraulic structures,such as pumping stations,sluices and dams.In order to ensure the safe and reliable operation of the project,it is necessary to observe and measure the settlement,expansion joints and seepage flow of hydraulic structures regularly and accurately.In this paper,an online correlation analysis and clustering method for multichannel real-time monitoring data streams was proposed.It aimed at finding the connections between data streams from multiple interested measuring channels,and automatically discovering potential project security problems and sensor failures.Firstly,the real-time data streams were continuously collected by recording sensor data from multiple measuring channels with the same frequency and aligning them on the time axis.Secondly,3 statistical features of the data streams were incrementally calculated.By employing the statistical features,the calculation of correlation coefficients of any 2 data streams could only run in 0(1) time.Thirdly,the clustering algorithm of density-based spatial clustering of applications with noise was used in order to automatically find grouped data streams with strong correlations and noised data streams with weak or without correlations.By analyzing the clustering results according to project related characteristics and objective laws,potential project safety risks as well as sensor failures could be identified.Based on an earlier developed safety monitoring system for Taizhou Gaogang sluice-pump station project,the experiments were carried out to analyze and cluster multichannel data streams of uplift pressure,expansion joint and temperature online.It took less than 1 s to process multiple data streams for one time.The clustering results of the water level data streams revealed that the water levels in the uplift pressure tubes installed in 5 sections of the project had strong positive relations owing to the normal action of ground water penetration.Exceptionally,the variation of water level in 1 tube was highly affected by water level change of the Yangtze River,which means there existed an abnormal seepage in that position.The failure of 1 uplift pressure sensor was also found according to the clustering results.Besides,the clustering results of the data streams of expansion joint size and temperature could be explained by thermal expansion and contraction.Especially,the expansion joint sizes of most places in the east-west direction of the horizontal plane had strong negative correlations to the environment temperature while the ones in the other directions were less affected.All the data streams classified as the noises could be directly used to discover the abnormal situations of the corresponding sensors.In conclusion,the proposed method could effectively find the connections between the online data streams from multiple interested measuring channels,and discover potential project safety problems and sensor failures.It showed to be an effective way to supplement the online data analysis methods in the hydraulic area.
Sluice-pump station projects usually consist of many widely distributed hydraulic structures,such as pumping stations,sluices and dams.In order to ensure the safe and reliable operation of the project,it is necessary to observe and measure the settlement,expansion joints and seepage flow of hydraulic structures regularly and accurately.In this paper,an online correlation analysis and clustering method for multichannel real-time monitoring data streams was proposed.It aimed at finding the connections between data streams from multiple interested measuring channels,and automatically discovering potential project security problems and sensor failures.Firstly,the real-time data streams were continuously collected by recording sensor data from multiple measuring channels with the same frequency and aligning them on the time axis.Secondly,3 statistical features of the data streams were incrementally calculated.By employing the statistical features,the calculation of correlation coefficients of any 2 data streams could only run in 0(1) time.Thirdly,the clustering algorithm of density-based spatial clustering of applications with noise was used in order to automatically find grouped data streams with strong correlations and noised data streams with weak or without correlations.By analyzing the clustering results according to project related characteristics and objective laws,potential project safety risks as well as sensor failures could be identified.Based on an earlier developed safety monitoring system for Taizhou Gaogang sluice-pump station project,the experiments were carried out to analyze and cluster multichannel data streams of uplift pressure,expansion joint and temperature online.It took less than 1 s to process multiple data streams for one time.The clustering results of the water level data streams revealed that the water levels in the uplift pressure tubes installed in 5 sections of the project had strong positive relations owing to the normal action of ground water penetration.Exceptionally,the variation of water level in 1 tube was highly affected by water level change of the Yangtze River,which means there existed an abnormal seepage in that position.The failure of 1 uplift pressure sensor was also found according to the clustering results.Besides,the clustering results of the data streams of expansion joint size and temperature could be explained by thermal expansion and contraction.Especially,the expansion joint sizes of most places in the east-west direction of the horizontal plane had strong negative correlations to the environment temperature while the ones in the other directions were less affected.All the data streams classified as the noises could be directly used to discover the abnormal situations of the corresponding sensors.In conclusion,the proposed method could effectively find the connections between the online data streams from multiple interested measuring channels,and discover potential project safety problems and sensor failures.It showed to be an effective way to supplement the online data analysis methods in the hydraulic area.
引文
[1]顾昊,王霞.自动监测技术在闸站工程变形观测中的应用[J].水利建设与管理,2015,35(3):56-59.Gu Hao,Wang Xia.Application of automatic monitoring technology in gate station[J].Water Resources Development&Management,2015,35(3):56-59.(in Chinese with English abstract)
[2]Shao Chenfei,Gu Chongshi,Yang Meng,et al.A novel model of dam displacement based on panel data[J/OL].Structural Control and Health Monitoring,2018,25(1):e2037.doi.org/10.1002/stc.2037.
[3]钱福军,唐鸿儒,包加桐,等.基于互联网的水利枢纽工程安全监测系统开发[J].人民长江,2016,47(5):98-101.Qian Fujun,Tang Hongru,Bao Jiatong,et al.Development of safety monitoring system for water project based on internet[J].Yangtze River,2016,47(5):98-101.(in Chinese with English abstract)
[4]金有杰,周克明,雷雨.基于移动终端的大坝安全监测信息发布平台研究[J].人民长江,2017,48(8):92-96.Jin Youjie,Zhou Keming,Lei Yu.Research on dam safety monitoring information publishing platform based on mobile terminal[J].Yangtze River,2017,48(8):92-96.(in Chinese with English abstract)
[5]Yang Jie,Bao Tiandong,Liang Desheng,et al.Management information system for dam safety monitoring based on B/Sstructure[C]//International Conference on Information Science and Engineering,2009:2332-2335.
[6]Wang Ligang,Yang Xiaocong,He Manchao.Research on safety monitoring system of tailings dam based on Internet of Things[J/OL].IOP Conference Series:Materials Science and Engineering,2018,322(5):052007.doi.org/10.1088/1757-899X/322/5/052007.
[7]桂中华,张浩,孙慧芳,等.水电机组振动劣化预警模型研究及应用[J].水利学报,2018,49(2):216-222.Gui Zhonghua,Zhang Hao,Sun Huifang,et al.Research and application of early warning model of vibration deterioration for hydroelectric-generator unit[J].Journal of Hydraulic Engineering,2018,49(2):216-222.(in Chinese with English abstract)
[8]秦浩,李同春,唐繁,等.基于MATLAB GUI的水电工程安全监测数据处理界面设计[J].水利水电技术,2016,47(4):70-74.Qin Hao,Li Tongchun,Tang Fan,et al.MATLAB GUI-based design of data processing interface for safety monitoring of hydropower project[J].Water Resources and Hydropower Engineering,2016,47(4):70-74.(in Chinese with English abstract)
[9]杨杰,方俊,胡德秀,等.偏最小二乘法回归在水利工程安全监测中的应用[J].农业工程学报,2007,23(3):136-140.Yang Jie,Fang Jun,Hu Dexiu,et al.Application of partial least-squares regression to safety monitoring of water conservancy projects[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(3):136-140.(in Chinese with English abstract)
[10]胡德秀,郭盼,陈诗怡,等.基于最小截平方和估计的监测数据分析方法[J].数理统计与管理,2017,36(4):632-640.Hu Dexiu,Guo Pan,Chen Shiyi,et al.Analysis method of a water engineering safety monitoring data based on the least trimmed square estimation[J].Journal of Applied Statistics and Management,2017,36(4):632-640.(in Chinese with English abstract)
[11]解建仓,王玥,雷社平,等.基于ARIMA模型的大坝安全监测数据分析与预测[J].人民黄河,2018,40(10):131-134.Xie Jiancang,Wang Yue,Lei Sheping,et al.Analysis and prediction of dam safety monitoring data based on ARIMAmodel[J].Yellow River,2018,40(10):131-134.(in Chinese with English abstract)
[12]He Jinping,Shi Yuqun.Dam safety fusion evaluation based on fuzzy pattern recognition[C]//International Conference on Computer Science and Service System,2011:1177-1180.
[13]崔少英,包腾飞,裴尧尧,等.基于模糊数学的大坝安全监测数据处理方法[J].水电能源科学,2012,30(11):45-48.Cui Shaoying,Bao Tengfei,Pei Yaoyao,et al.Data processing of dam safety monitoring based on fuzzy mathematical approach[J].Water Resources and Power,2012,30(11):45-48.(in Chinese with English abstract)
[14]Su Huaizhi,Wen Zhiping,Gu Chongshi.An early-warning model of dam safety based on rough set theory and support vector machine[C]//International Conference on Machine Learing and Cybernectics,2006:3455-3460.
[15]Su Huaizhi,Chen Zhexin,Wen Zhiping.Performance improvement method of support vector machine-based model monitoring dam safety[J].Structural Control and Health Monitoring,2016,23(2):252-266.
[16]Gourine B,Khelifa S.Analysis of dam deformation using artificial neural networks methods and singular spectrum analysis[C]//Euro-Mediterranean Conference for Environmental Integration.Cham:Springer,2017:871-874.
[17]胡添翼,游孟陶,陆天琳,等.一种改进的空间相关系数在水库高边坡外观变形监测中的应用[J].长江科学院院报,2017,34(7):41-47,53.Hu Tianyi,You Mengtao,Lu Tianlin,et al.Application of an improved spatial correlation coefficient to exterior deformation monitoring of high slope in reservoir area[J].Journal of Yangtze River Scientific Research Institute,2017,34(7):41-47,53.(in Chinese with English abstract)
[18]Chen Bo,Zhang Li,Qian Qiupei,et al.Research on the seepage safety monitoring indexes of the high core rockfill dam[J].World Journal of Engineering and Technology,2017,5(3B):42-53.
[19]Santillan D,Fraile-Ardanuy J,Toledo M A.Dam seepage analysis based on artificial neural networks:The hysteresis phenomenon[C]//International Joint Conference on Neural Networks,2013:1-8.
[20]Bai Liang,Cheng Xueqi,Liang Jiye,et al.An optimization model for clustering categorical data streams with drifting concepts[J].IEEE Transactions on knowledge and data engineering,2016,28(11):2871-2883.
[21]Puschmann D,Barnaghi P,Tafazolli R.Adaptive clustering for dynamic IoT data streams[J].IEEE Internet of Things Journal,2017,4(1):64-74.
[22]Kaneriya A,Shukla M.A novel approach for clustering data streams using granularity technique[C]//International Conference on Advances in Computer Engineering and Applications,2015:586-590.
[23]Amini A,Saboohi H,Wah T.A multi density-based clustering algorithm for data stream with noise[C]//International Conference on Data Mining Workshops,2013:1105-1112.
[24]Sauvanaud C,Silvestre G,Kaaniche M,et al.Data stream clustering for online anomaly detection in cloud applications[C]//European Dependable Computing Conference,2015:120-131.
[25]Comito C,Pizzuti C,Procopio N.Online clustering for topic detection in social data streams[C]//International Conference on Tools with Artificial Intelligence.USA:IEEE,2016:362-369.
[26]Yin Chunyong,Xia Lian,Wang Jin.Application of an improved data stream clustering algorithm in intrusion detection system[M]//James J,Park J,Chen S,et al.Advanced Multimedia and Ubiquitous Engineering.Cham:Springer,2017:626-632.
[27]Maulik U,Saha I.Automatic fuzzy clustering using modified differential evolution for image classification[J].IEEETransactions on Geoscience&Remote Sensing,2010,48(9):3503-3510.
[28]Savkare S S,Narote A S,Narote S P.Comparative analysis of segmentation algorithms using threshold and K-Mean Clustering[C]//International Symposium on Intelligent Systems Technologies and Applications.Springer International Publishing,2016:111-118.
[29]Tu Li,Chen Ling,Zou Lingjun.Clustering multiple data streams based on correlation analysis[J].Journal of Software,2009,20(7):1756-1767.
[30]Ester M,Kriegel H P,Xu X.A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]//International Conference on Knowledge Discovery&Data Mining.USA:AAAI,1996:226-231.
[2]Shao Chenfei,Gu Chongshi,Yang Meng,et al.A novel model of dam displacement based on panel data[J/OL].Structural Control and Health Monitoring,2018,25(1):e2037.doi.org/10.1002/stc.2037.
[3]钱福军,唐鸿儒,包加桐,等.基于互联网的水利枢纽工程安全监测系统开发[J].人民长江,2016,47(5):98-101.Qian Fujun,Tang Hongru,Bao Jiatong,et al.Development of safety monitoring system for water project based on internet[J].Yangtze River,2016,47(5):98-101.(in Chinese with English abstract)
[4]金有杰,周克明,雷雨.基于移动终端的大坝安全监测信息发布平台研究[J].人民长江,2017,48(8):92-96.Jin Youjie,Zhou Keming,Lei Yu.Research on dam safety monitoring information publishing platform based on mobile terminal[J].Yangtze River,2017,48(8):92-96.(in Chinese with English abstract)
[5]Yang Jie,Bao Tiandong,Liang Desheng,et al.Management information system for dam safety monitoring based on B/Sstructure[C]//International Conference on Information Science and Engineering,2009:2332-2335.
[6]Wang Ligang,Yang Xiaocong,He Manchao.Research on safety monitoring system of tailings dam based on Internet of Things[J/OL].IOP Conference Series:Materials Science and Engineering,2018,322(5):052007.doi.org/10.1088/1757-899X/322/5/052007.
[7]桂中华,张浩,孙慧芳,等.水电机组振动劣化预警模型研究及应用[J].水利学报,2018,49(2):216-222.Gui Zhonghua,Zhang Hao,Sun Huifang,et al.Research and application of early warning model of vibration deterioration for hydroelectric-generator unit[J].Journal of Hydraulic Engineering,2018,49(2):216-222.(in Chinese with English abstract)
[8]秦浩,李同春,唐繁,等.基于MATLAB GUI的水电工程安全监测数据处理界面设计[J].水利水电技术,2016,47(4):70-74.Qin Hao,Li Tongchun,Tang Fan,et al.MATLAB GUI-based design of data processing interface for safety monitoring of hydropower project[J].Water Resources and Hydropower Engineering,2016,47(4):70-74.(in Chinese with English abstract)
[9]杨杰,方俊,胡德秀,等.偏最小二乘法回归在水利工程安全监测中的应用[J].农业工程学报,2007,23(3):136-140.Yang Jie,Fang Jun,Hu Dexiu,et al.Application of partial least-squares regression to safety monitoring of water conservancy projects[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(3):136-140.(in Chinese with English abstract)
[10]胡德秀,郭盼,陈诗怡,等.基于最小截平方和估计的监测数据分析方法[J].数理统计与管理,2017,36(4):632-640.Hu Dexiu,Guo Pan,Chen Shiyi,et al.Analysis method of a water engineering safety monitoring data based on the least trimmed square estimation[J].Journal of Applied Statistics and Management,2017,36(4):632-640.(in Chinese with English abstract)
[11]解建仓,王玥,雷社平,等.基于ARIMA模型的大坝安全监测数据分析与预测[J].人民黄河,2018,40(10):131-134.Xie Jiancang,Wang Yue,Lei Sheping,et al.Analysis and prediction of dam safety monitoring data based on ARIMAmodel[J].Yellow River,2018,40(10):131-134.(in Chinese with English abstract)
[12]He Jinping,Shi Yuqun.Dam safety fusion evaluation based on fuzzy pattern recognition[C]//International Conference on Computer Science and Service System,2011:1177-1180.
[13]崔少英,包腾飞,裴尧尧,等.基于模糊数学的大坝安全监测数据处理方法[J].水电能源科学,2012,30(11):45-48.Cui Shaoying,Bao Tengfei,Pei Yaoyao,et al.Data processing of dam safety monitoring based on fuzzy mathematical approach[J].Water Resources and Power,2012,30(11):45-48.(in Chinese with English abstract)
[14]Su Huaizhi,Wen Zhiping,Gu Chongshi.An early-warning model of dam safety based on rough set theory and support vector machine[C]//International Conference on Machine Learing and Cybernectics,2006:3455-3460.
[15]Su Huaizhi,Chen Zhexin,Wen Zhiping.Performance improvement method of support vector machine-based model monitoring dam safety[J].Structural Control and Health Monitoring,2016,23(2):252-266.
[16]Gourine B,Khelifa S.Analysis of dam deformation using artificial neural networks methods and singular spectrum analysis[C]//Euro-Mediterranean Conference for Environmental Integration.Cham:Springer,2017:871-874.
[17]胡添翼,游孟陶,陆天琳,等.一种改进的空间相关系数在水库高边坡外观变形监测中的应用[J].长江科学院院报,2017,34(7):41-47,53.Hu Tianyi,You Mengtao,Lu Tianlin,et al.Application of an improved spatial correlation coefficient to exterior deformation monitoring of high slope in reservoir area[J].Journal of Yangtze River Scientific Research Institute,2017,34(7):41-47,53.(in Chinese with English abstract)
[18]Chen Bo,Zhang Li,Qian Qiupei,et al.Research on the seepage safety monitoring indexes of the high core rockfill dam[J].World Journal of Engineering and Technology,2017,5(3B):42-53.
[19]Santillan D,Fraile-Ardanuy J,Toledo M A.Dam seepage analysis based on artificial neural networks:The hysteresis phenomenon[C]//International Joint Conference on Neural Networks,2013:1-8.
[20]Bai Liang,Cheng Xueqi,Liang Jiye,et al.An optimization model for clustering categorical data streams with drifting concepts[J].IEEE Transactions on knowledge and data engineering,2016,28(11):2871-2883.
[21]Puschmann D,Barnaghi P,Tafazolli R.Adaptive clustering for dynamic IoT data streams[J].IEEE Internet of Things Journal,2017,4(1):64-74.
[22]Kaneriya A,Shukla M.A novel approach for clustering data streams using granularity technique[C]//International Conference on Advances in Computer Engineering and Applications,2015:586-590.
[23]Amini A,Saboohi H,Wah T.A multi density-based clustering algorithm for data stream with noise[C]//International Conference on Data Mining Workshops,2013:1105-1112.
[24]Sauvanaud C,Silvestre G,Kaaniche M,et al.Data stream clustering for online anomaly detection in cloud applications[C]//European Dependable Computing Conference,2015:120-131.
[25]Comito C,Pizzuti C,Procopio N.Online clustering for topic detection in social data streams[C]//International Conference on Tools with Artificial Intelligence.USA:IEEE,2016:362-369.
[26]Yin Chunyong,Xia Lian,Wang Jin.Application of an improved data stream clustering algorithm in intrusion detection system[M]//James J,Park J,Chen S,et al.Advanced Multimedia and Ubiquitous Engineering.Cham:Springer,2017:626-632.
[27]Maulik U,Saha I.Automatic fuzzy clustering using modified differential evolution for image classification[J].IEEETransactions on Geoscience&Remote Sensing,2010,48(9):3503-3510.
[28]Savkare S S,Narote A S,Narote S P.Comparative analysis of segmentation algorithms using threshold and K-Mean Clustering[C]//International Symposium on Intelligent Systems Technologies and Applications.Springer International Publishing,2016:111-118.
[29]Tu Li,Chen Ling,Zou Lingjun.Clustering multiple data streams based on correlation analysis[J].Journal of Software,2009,20(7):1756-1767.
[30]Ester M,Kriegel H P,Xu X.A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]//International Conference on Knowledge Discovery&Data Mining.USA:AAAI,1996:226-231.