Helicopter turboshaft modeling via mixtures of experts

A reliable and robust engine model is critical for helicopter design, operation, and maintenance, given the centrality of this sub-system. Several sources of uncertainty can limit the reliability and fidelity of first-principles models, necessitating data-driven solutions. Due to the relevant safety and security issues inherent to aircraft operation, however, fully black-box models may be unsuited to the challenge, due to their lack of explainability. In this work, we propose a multi-model approach to combine multiple physics-based descriptions, achieving a learning architecture that incorporates, in a data-driven setting, the existing knowledge of the engine’s dynamics, maximizing interpretability and facilitating model validation and diagnostics. Enabled by recent advances in onboard data collection, we learn the model directly on realistic operating conditions by leveraging recorded flight information. The benefits include a high degree of local interpretability as well as minimal requirements in terms of input signals, as empirically demonstrated in a real-world use case. We compare our technique on a real helicopter dataset against the SINDy technique, showcasing the advantages of our approach against the well-known interpretable approach to Nonlinear System Identification.