Optimal Adaptive System Health Monitoring and Diagnosis for Resource Constrained Cyber-Physical Systems

Cyber-physical systems (CPS) are complex net-centric hardware/software systems that can be applied to transportation, healthcare, defense, and other real-time applications. To meet the high reliability and safety requirements for these systems, proactive system health monitoring and management (HMM) techniques can be used. However, to be effective, it is necessary to ensure that the operation of the underlying HMM system does not adversely impact the normal operation of the system being monitored. In particular, it must be ensured that the operation of the HMM system will not lead to resource contentions that may prevent the system being monitored from timely completion of critical tasks. This paper presents an adaptive HMM system model that defines the fault diagnosis quality metrics and supports diagnosis requirement specifications. Based on the model, the sensor activation decision problem (SADP) is defined along with a steepest descent based heuristic algorithm to make the HMM configuration decisions that best satisfy the diagnosis quality requirements. Evaluation results show that the technique reduces the overall system resource consumption without adversely impacting the diagnosis capability of the HMM.