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

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。

Safety monitoring of sluice-pump station project based on online correlation analysis and clustering of multichannel data streams

Abstract: 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.