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

TL;DR

This paper introduces the Constant-Gain Equation-Error Method (CG-EEM), a robust and efficient approach for monitoring airliner aerodynamics using QAR data, overcoming limitations of traditional filters and estimators.

Contribution

The paper develops and validates a novel constant-gain estimator tailored for low-excitation cruise data, enabling reliable fleet-wide aerodynamic performance monitoring.

Findings

CG-EEM produces consistent, physically plausible aerodynamic parameters.

It correctly identifies performance differences between aircraft types.

The method is validated on over 200 flights, demonstrating robustness and scalability.

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.