In this paper, a self-diagnosis system of observer fault with linear and non-linear combination is studied in light of the unstable performance of the automatic monitoring system and the drift of the measured value. The system makes a prediction step ahead of time, compares it with the online measured value, and makes a logical judgment based on the residual error to achieve the purpose of real-time diagnosis of the automatic monitoring system. We developed a novel combined algorithm for dam deformation prediction using two traditional models and one optimization model. The developed algorithm combines two sub-algorithms: the gray model (GM) (1, 1) and the back-propagation neural network (BPNN) model. The GM (1, 1) addresses the effects of the automated monitoring of data from unstable situations; the BPNN model addresses the internal non-linear regularity of the dam displacement. The connection weights and thresholds of the BPNN model can be optimized and determined via the genetic algorithm (GA), which can decrease the uncertainties within the model predictions and improve the prediction accuracy. The results show that the fault self-diagnosis system based on the GM-GA-BP combined model can realize online fault diagnosis better than the traditional single models.

In this paper, the subsidence change maps in Langfang were obtained using ASAR images covering Langfang area acquired from August 2007 to September 2010 with the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique. Surface subsidence spatial-temporal characteristics in Langfang was investigated. The analysis of experimental results show that 1) prominent uneven subsidence patterns were identified in Langfang. Specifically, surface subsidence rates ranged from −77 mm/year to 4 mm/year, and maximum subsidence rate was detected in Nanjianta Town; 2) the standard deviation of the target point subsidence rates in the study area ranges from 0.8 mm/year to 9.3 mm/year, 70.4% of the monitoring point standard deviations in study area are less than 3 mm/year; 3) surface subsidence time series presented nonlinear variation trend. The surface subsidence has a tendency to gradually expand around the center of Nanjianta Town, and the scope and trend of expansion to the north and west of Langfang are more serious.