A Constant-Gain Equation-Error Framework for Airliner Aerodynamic Monitoring Using QAR Data
For airlines and maintenance teams, this provides a scalable tool for early detection of aerodynamic performance degradation using operational data.
The paper tackles the challenge of monitoring in-service aerodynamic performance of airliners using QAR data, where conventional methods fail due to missing parameters and low-excitation cruise data. The proposed Constant-Gain Equation-Error Method (CG-EEM) produces consistent aerodynamic parameters across over 200 flights, enabling robust fleet-wide performance monitoring.
Monitoring the in-service aerodynamic performance of airliners is critical for operational efficiency and safety, but using operational Quick Access Recorder (QAR) data for this purpose presents significant challenges. This paper first establishes that the absence of key parameters, particularly aircraft moments of inertia, makes conventional state-propagation filters fundamentally unsuitable for this application. This limitation necessitates a decoupled, Equation-Error Method (EEM). However, we then demonstrate through a comparative analysis that standard recursive estimators with time-varying gains, such as Recursive Least Squares (RLS), also fail within an EEM framework, exhibiting premature convergence or instability when applied to low-excitation cruise data. To overcome these dual challenges, we propose and validate the Constant-Gain Equation-Error Method (CG-EEM). This framework employs a custom estimator with a constant, Kalman-like gain, which is perfectly suited to the stationary, low-signal-to-noise characteristics of cruise flight. The CG-EEM is extensively validated on a large, multi-fleet dataset of over 200 flights, where it produces highly consistent, physically plausible aerodynamic parameters and correctly identifies known performance differences between aircraft types. The result is a robust, scalable, and computationally efficient tool for fleet-wide performance monitoring and the early detection of performance degradation.