A Constant-Gain Equation-Error Framework for Airliner Aerodynamic Monitoring Using QAR Data

TL;DR

The paper tackles the challenge of in-service aerodynamic monitoring using QAR data, where missing mass properties prevent reliable state propagation. It introduces the Constant-Gain Equation-Error Method (CG-EEM), a decoupled, output-error identification framework that uses measured QAR states in an algebraic aerodynamic-force model and employs a constant-gain estimator for robust parameter estimation in low-excitation cruise data. Key contributions include demonstrating the superiority of CG-EEM over traditional time-varying gain methods, establishing the necessity of a parabolic drag model for global consistency, and validating the approach on a large, multi-fleet dataset to yield physically meaningful, fleet-wide aerodynamic parameters. The method enables scalable, low-cost fleet monitoring and early detection of performance degradation, with practical impact for efficiency and safety in commercial aviation.

Abstract

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.

Figures (9)

• Figure 1: Comparison of integrated pitch rate and measured pitch angle, demonstrating significant deviation due to sensor noise.
• Figure 2: Flowchart of the proposed aerodynamic parameter identification framework, from raw QAR data to final identified aerodynamic parameters.
• Figure 3: CG-EEM parameter convergence on simulated data. The time histories of the key aerodynamic parameter estimates (solid blue lines) are shown. The estimates demonstrate rapid and stable convergence from an initial 0 values to the known ground truth values (dashed grey lines). This verifies the fundamental integrity and accuracy of the CG-EEM algorithm.
• Figure 4: Fidelity of the converged model on simulated data. The top panels compare the accelerations predicted by the final identified model against the pseudo-QAR measurement data (grey). The bottom panels show the time history of the residual error. The model's predictions closely track the noisy measurements, and the residual errors are small, confirming that the identified parameter set provides a high-fidelity representation of the system dynamics.
• Figure 5: Time histories of key aerodynamic parameter estimates. All parameters demonstrate rapid convergence to stable and physically plausible values, confirming their identifiability.