Neural network learning for blind source separation with application in dam safety monitoring
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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The monitoring and behavioral prediction of the hydrodams and hydrotechnical sites relies on the analysis of some objective information, the large number of sensors and examination modalities renders the human inspection of this information very difficult if not even impossible. The main objective of system is to provide a solution to overcome this problem through the development of a decision support system with an expert system component, able to minimize the subjectivity of the human expert in monitoring and behavioral prediction of the hydrotechnical structures and sites. The present article describe an integrated system, decisional support based on multisensorial information fusion provided by supervisor sensors from dams and hydropower plants, related to meteorological and geophysical factors, that can achieved behavior surveillance and prediction of dams. So, the system can provide the detection, diagnosis and monitoring of a structure defects or other functional anomalies, can also, forewarn, through the prediction component, the development of danger.