Remaining Life Estimation Using Peer Equipment Data

The patent creates virtual copies of equipment by developing digital twins that replicate the behavior and degradation patterns of physical equipment. These digital copies allow analysts to study failure modes and predict remaining life without requiring actual run-to-end-of-life data from physical equipment, as the digital twins can be configured to simulate various failure scenarios.

The system performs preliminary damage propagation modeling to predict future equipment states before actual failure occurs. By using physics-based models to simulate damage accumulation and propagation, the system can estimate remaining life based on current equipment state and projected future conditions, rather than requiring historical data from complete failure cycles.

The patent segments the equipment into hierarchical levels (system level, subsystem level, component level) and applies appropriate prediction methods at each level. This allows the system to provide both fleet-level reliability forecasting and component-level prognostic insights by breaking down the complex prediction problem into manageable segments that can be analyzed independently and then integrated.

The system creates a multi-functional prognostic platform that serves multiple purposes: fleet-wide reliability forecasting, component-level remaining life prediction, maintenance optimization, and failure mode analysis. This universal approach allows the same system to address both high-level mission reliability concerns and detailed component-level diagnostic needs.

The patent applies local quality by using detailed physics-based damage propagation models only for critical components where high accuracy is essential, while using simpler data-driven or peer-based approaches for less critical parts. This selective application of modeling complexity optimizes resource allocation by concentrating computational and developmental efforts on components that most impact overall system reliability.

The system creates a composite prognostic approach that combines multiple modeling techniques (physics-based damage propagation, data-driven pattern recognition, and peer-based reliability forecasting) into an integrated framework. This composite methodology leverages the strengths of each approach while mitigating their individual weaknesses, providing accurate predictions without the prohibitive costs of applying detailed finite element modeling system-wide.