Inconsistent sensor data detection/correction: application to environmental systems
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In this paper, a data detection/correction approach is proposed for a real environmental monitoring system, in order to provide a reliable dataset when sensor faults occur. This is the case of communication faults that may prevent the acquisition of a complete dataset, which is of paramount importance in order to successfully apply further system tasks such as fault diagnosis. Sensor detection/correction method presented here is based on the combined used of spatial and time series models. Spatial models take advantage of the physical relation between different variables emplaced in the system (temperature sensors here) while time series models take advantage of the temporal redundancy of the measured variables, by means of Holt-Winters models here. The proposed approach is successfully applied to the rock collapse forecasting system in the Torrioni di Rialba located in Lombardy (Italy).
CitationCuguero, M.A. [et al.]. Inconsistent sensor data detection/correction: application to environmental systems. A: IEEE International Joint Conference on Neural Networks. "Proceedings IEEE 2014 International Joint Conference on Neural Networks". Beijing: 2014, p. 84-90.
DLIEEE catalog number CFP14IJS-CDR