Multifidelity Framework for the Efficient Identification of Damages in Complex Aerospace Systems

Abstract

Next generation aircraft require the development and integration of a deal of innovative technologies to meet the ambitious sustainability goals set for aviation. This transformational effort is associated with a tremendous increase of the complexity of the onboard systems and their multiphysics coupled behaviours. A critical aspect relates to the characterization of the coupled fault modes resulting from the integration of these novel technologies, which introduce identifiability issues and demand new approaches for the efficient identification of (multimodal) non-nominal conditions. Model-based fault detection and identification (FDI) has been proven essential to infer onboard systems' health from signal acquisitions, but existing methods are too expensive and fail to capture incipient faults in presence of multimodality which prevent scaling to complex multiphysics systems. This work introduces a multifidelity framework to accelerate the identification of fault modes affecting complex systems. An original two-stage compression computes an optimally informative and highly reduced representation of the monitoring signals for the minimum demand of onboard resources. A multifidelity scheme for Bayesian inversion is developed to infer multidomain fault parameters from the compressed signals: variable cost and fidelity models are optimally queried for a major reduction of the overall computational expense. The framework is demonstrated and validated for aerospace electromechanical actuators affected by incipient multimodal faults. Remarkable accelerations of the FDI procedure are observed and the exact identification of the incipient fault condition achieved one order of magnitude faster than with standard algorithms.