Regime recognition is a critical tool used for condition-based maintenance, fatigue life prediction, and creation of usage spectra for military and commercial rotorcraft. While a variety of regime recognition algorithms are currently in use, many current algorithms suffer from an over-reliance on training data or poor classification accuracy with respect to the stringent guidelines outlined in ADS-79E. This paper introduces a new type of regime recognition algorithm based on a multiple model adaptive estimation scheme, known as an interacting multiple model (IMM) estimator. IMM estimators use a bank of dynamic models to evaluate the likelihood of the system existing in one of various possible dynamic modes. In the regime recognition context, each mode represents the system operating in a given maneuver regime. Compared with other approaches, IMM estimators offer the benefits of probabilistic regime classification and the incorporation of knowledge of the aircraft flight dynamics, which reduces reliance on training data. This paper presents a novel formulation of an IMM estimator for regime recognition wherein mode probabilities from a bank of IMM filters are combined in Bayesian framework to yield maneuver regime probabilities. Example results for the SH-60B show favorable classification performance in preliminary simulation studies using common maneuvers.
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