Interacting Multiple Model Estimation for Helicopter Regime Recognition

Abstract

Regime recognition (RR) is an important aspect of condition-based maintenance for modern helicopters. RR involves the postflight classification of flight data into regime categories. These classifications are then used to predict fatigue damage and vehicle usage spectra. Although several RR algorithms have been proposed to date, many suffer from an overreliance on training data or poor accuracy when presented with flight data that do not precisely match one of the defined regimes. This paper introduces a new type of RR algorithm based on interacting multiple model (IMM) estimators. IMM estimators use a bank of dynamic models to evaluate the probability of the system existing in one of various possible dynamic modes. In the RR context, each of the dynamic modes corresponds to a particular regime. The proposed recognition algorithm offers advantages over other methods in that it provides a probabilistic classification of flight data, thereby explicitly acknowledging uncertainty in the recognition process. Furthermore, the algorithm is model based, reducing reliance on training data. Following a detailed description of the methodology, results are provided by applying the algorithm to both simulated and actual flight test data. Results show significant performance improvements compared with a typical rule-based recognition scheme.