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.