%0 Journal Article
%T Sensor Fusion for Fault Detection & Classification in Distributed Physical Processes
%A Soumalya Sarkar
%A Soumik Sarkar
%A Nurali Virani
%A Asok Ray
%A Murat Yasar
%J Frontiers in Robotics and AI
%D 2014
%I Frontiers Media
%R 10.3389/frobt.2014.00016
%X This paper proposes a feature extraction and fusion methodology to perform fault detection & classification in distributed physical processes generating heterogeneous data. The underlying concept is built upon a semantic framework for multi-sensor data interpretation using graphical models of Probabilistic Finite State Automata (PFSA).While the computational complexity is reduced by pruning the fused graphical model using an information-theoretic approach, the algorithms are developed to achieve high reliability via retaining the essential spatiotemporal characteristics of the physical processes. The concept has been validated on a simulation test bed of distributed shipboard auxiliary systems.
%K fault detection
%K Sensor Fusion
%K Spatiotemporal pattern
%K Sensor network pruning
%K Symbolic dynamics
%U http://www.frontiersin.org/Journal/10.3389/frobt.2014.00016/abstract