Intelligent decision support systems on the grid for the health monitoring of complex systems

The demand for increased functionality of systems has lead to the complexity of systems, as measured by the number of components and classes of behaviours, to grow steadily over the years. The analysis of such complex systems, for example towards the goal of health monitoring, with a single methodology is bound to be inadequate. In this presentation, a grid enabled health monitoring system for a fleet of aircraft engines developed in the Distributed Aircraft Maintenance Environment (DAME) project, a UK Research Council funded E-Science Pilot Project, is described. The collection of tools in the intelligent decision support for data analysis include algorithms for feature detection from engine vibration signals, neural networks based time series pattern matching and case based reasoning based decision making. To facilitate users collaboration, intelligent grid workflow advisor system is proposed. The DAME system is then extended to include proactive mobile computing which provides the mobile decision support functionality. Finally, future directions for grid computing based health monitoring systems are discussed.