Research on Gap-filling Algorithms for Dike Health Monitoring Systems

Earthen dam (dike/levee) health monitoring is a challenging task. Monitoring algorithms have to detect anomalies in dike behavior in on-line mode basing on measurement collected from the sensors installed in the dike. One of the important monitoring-related challenges for dike health monitoring is the quality of sensors measurements. There are often gaps that occur due to failures, outages of transmission or data collection systems, incorrect configurations of systems and other internal and external factors, therefore it is necessary to improve the quality measurement using specific algorithms. The proposed approach is based on adaptive algorithms filling in gaps in sensor measurements in the conditions of a priori uncertainty of signals models. The algorithms (based on the autoregressive model, Caterpillar-SSA, Fourier transform) presented in this paper use historical data for signal reconciliation. The description and analysis of the algorithms are also included in the paper. The algorithms have been tested at the Boston dike, Great Britain. The research findings and algorithms are implemented by Siemens in the UrbanFlood Early Warning System.