Spatiotemporal information fusion for fault detection in shipboard auxiliary systems

This paper addresses the issues of data analysis and sensor fusion that are critical for information management leading to (real-time) fault detection and classification in distributed physical processes (e.g., shipboard auxiliary systems). The proposed technique utilizes a semantic framework for multi-sensor data modeling, where the complexity is reduced by pruning the sensor network through an information-theoretic (e.g., mutual information-based) approach. The underlying algorithms are developed to achieve high reliability and computational efficiency while retaining the essential spatiotemporal characteristics of the physical system. The concept is validated on a simulation test bed of shipboard auxiliary systems.