Defect Identification by Sensor Network Under Uncertainties

Abstract

This paper presents a theoretical framework for identification of defects by a sensor network under uncertainties. While location of sensors are not known due to their inspection due to limited knowledge on the structure to be inspected, existing inspection methods do not take uncertainties of sensor locations into account for the localization of defects. The proposed theoretical framework formulates the uncertainties of sensor states stemming from both motion and measurement and allows stochastic identification of defects using recursive Beyesian estimation. Multi-sensor belief fusion further allows a network of sensors to jointly identify defects and improve the accuracy of identification. Parametric studies and application to practical defect identification have shown the validity of the proposed framework.

Multisensor methods to estimate thermal diffusivity

“…The approach is applied to tumor modeling and analysis. Another related work is that of Furukawa [2] who takes location uncertainty into account when localizing defects and "formulates the uncertainties of sensor states stemming from both motion and measurement and allows stochastic identification of defects using recursive Bayesian estimation. "…”