The Best Regime Recognition Algorithm for HUMS

Usage monitoring for Health and Usage Monitoring Systems (HUMS) equipped aircraft entails determining the actual usage of a component over time. This allows the actually usage/damage from a flight to be assigning to that component instead of the more conservative, worst case usage. By measuring the actually usage on the aircraft, the life of components can be extended, resulting in reduced maintenance cost. Alternatively, for aggressively flown aircraft, safety is maintained in that a component usage may be consumed to a greater extent than expected by the original flight spectrum data. Usage Monitoring requires an accurate representation of regime (e.g. Regime Recognition (RR)), where regime is the flight profile of the aircraft at each instant of flight time. For each regime, there is a usage assigned for each component that is life limited (e.g. accumulated damage over time). For example, the damage accumulated for a component would be higher if the aircraft is undergoing a high G maneuver vs. straight and level flight. In the past, RR algorithms have used logical tests (Goodrich IVHMU or that presented in Ref [1]) or neural networks (VMEP, Ref [2]). Logical tests can be prone to errors due to inherently noisy parameter used. Neural networks in general are difficult to certify and could require a large amount of training data. Other methods, such as Markov Models (Ref [3]) show promise, but could be computationally expensive. Presented here is a noise tolerant algorithm that does not present the problems associated with: logical test (dealing with noise), certification of neural networks, or computational complexities.