A model based approach to constructing performance degradation monitoring systems

Enterprises that are tightly choreographed require ongoing knowledge of their member systems capabilities in order to adapt and maintain effective operations. Member system capabilities are derived from knowing the current performance levels of the member systems functionalities. The performance degradation curves for the member systems functionalities must be continually monitored. There are several basic stakeholders in the performance degradation curves. Operations can use them to manage the aggregate mission performance. The operator can use them to adapt the vehicle to the mission. Sustainment, anticipatory logistics, and maintenance clearly have a need for this information. By contrast, loosely coupled or uncoupled enterprises require less knowledge of degraded performance; though they still require knowledge of complete loss of performance, i.e., failure. Typically diagnostics are embedded into vehicle/plant components in the form of BIT tests; primarily for maintenance and operations. The ability to work with integrated component models induces a higher system level analysis and diagnosis since the reasoners work with coupled component behaviors. In this paper, we discuss an approach to designing and building an embedded vehicle performance monitoring (VPM) system that goes beyond embedding diagnostics into the individual vehicle components in the form of BIT tests. A model based reasoning strategy is used that assumes a simulation based design acquisition process for designing the vehicle. This approach leverages physics models of the vehicle components that are endemic to the simulation based design acquisition process. It also assumes that the design anticipates the need to make available the component-level signals to enable the embedded vehicle performance monitoring system. The simulation-based approach coupled with degradation and performance monitoring also sets up the ability to predict future vehicle behaviors and performance under degraded conditi- - ons