Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter

Abstract

Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalman Filter (STKF), a dynamic spatio-temporal filtering model, is used as a spatio-temporal data analysis method for dam deformation. There were three main steps in the method applied in this paper. The first step was to determine the Kriging spatial fields based on the characteristics of dam deformation. Next, the observation noise covariance, system noise covariance, the initial mean vector state, and its covariance were estimated using the Expectation Maximization algorithm (EM algorithm) in the second step. In the third step, we filtered the observation noise, interpolated the whole dam unmonitored data in space and time domains, and predicted the deformation for the whole dam using the Kalman filter recursion algorithm. The simulation data and Wuqiangxi dam deformation monitoring data were used to verify the STKF method. The results show that the STKF not only can filter the deformation data noise in both the temporal and spatial domain effectively, but also can interpolate and predict the deformation for the whole dam.

Study of a Gray Genetic BP Neural Network Model in Fault Monitoring and a Diagnosis System for Dam Safety

“…Next, the mean sequence Z (1) is generated with the accumulation generation sequence X where z (1) (m) is the mean value and can be generated with the accumulation generation sequence; finally, the gray differential equation of the GM (1, 1) is constructed as:…”

“…Thus, a gray system reveals the inherent regularity of a given data sequence through data mining and collation [55,56]. For GM (1,1), the original sequence is accumulated to generate an accumulation generation sequence that is then used to generate an accumulative reduction sequence. The GM (1, 1), a first-order, one-variable GM, is an effective forecasting method employed for issues with uncertain and imperfect information.…”

“…With the rapid development of science and technology, dam safety automation monitoring has undergone a qualitative leap [1][2][3][4]. While dam safety automatic monitoring has been vigorously promoted, this also necessitates new requirements for monitoring the stability and reliability of the automation system.…”

“…When the system deviates from normal operation or fails, the residuals deviate from zero and exhibit a large residual error. If the corresponding residual value of the predicted value and the online measured value exceeds the threshold, the system will activate an alarm to realize the real-time diagnosis of the performance non-linear characteristics of dam deformation, the water level and temperature of the dam area are adopted to correct the residual values of the GM (1,1). In this paper, the GA-BP model is adopted to establish the relationship between the influencing factors and the gray residual values.…”

“…Next, the new residuals can be predicted by the GA-BP model. Finally, the dam displacement prediction is obtained by subtracting the new residuals from the prediction of GM (1,1). The details are as follows:…”