Application of Bayesian Sensor Placement Optimization for Real–Time Health Monitoring

Sensors are being increasingly used for real–time health monitoring of complex systems. The measured quantities are expected to provide real–time information about the state of the system, its subsystems, components, and internal and external physical parameters. A complex system normally requires many sensors to extract required information from the sensed environment. The increasing costs of aging systems and infrastructures have become a major concern and real–time health monitoring systems could ensure increased safety and reliability of these systems. Real–time system health monitoring, assesses the state of systems’ health and, through appropriate data processing and interpretation, can predict the remaining life of the system. This paper introduces a method based on Bayesian networks and attempts to find optimum locations of sensors for the best estimate a system health. Information metrics are used for optimized sensor placement based on the value of information that each possible sensor placement scenario provides.