Data-driven Method for Estimating Aircraft Mass from Quick Access Recorder using Aircraft Dynamics and Multilayer Perceptron Neural Network

TL;DR

This paper introduces a universal, data-driven approach using neural networks and flight data to accurately estimate aircraft mass during initial climb, enhancing safety and operational efficiency without relying on proprietary parameters.

Contribution

The study develops a novel neural network-based method utilizing QAR data for aircraft mass estimation, independent of aircraft-specific parameters, applicable across all aircraft types.

Findings

Reasonable accuracy achieved on Boeing 777-300ER data

Method is universally applicable to different aircraft types

Enhances payload utilization and safety management

Abstract

Accurate aircraft-mass estimation is critical to airlines from the safety-management and performance-optimization viewpoints. Overloading an aircraft with passengers and baggage might result in a safety hazard. In contrast, not fully utilizing an aircraft's payload-carrying capacity undermines its operational efficiency and airline profitability. However, accurate determination of the aircraft mass for each operating flight is not feasible because it is impractical to weigh each aircraft component, including the payload. The existing methods for aircraft-mass estimation are dependent on the aircraft- and engine-performance parameters, which are usually considered proprietary information. Moreover, the values of these parameters vary under different operating conditions while those of others might be subject to large estimation errors. This paper presents a data-driven method involving use of the quick access recorder (QAR)-a digital flight-data recorder-installed on all aircrafts to record the initial aircraft climb mass during each flight. The method requires users to select appropriate parameters among several thousand others recorded by the QAR using physical models. The selected data are subsequently processed and provided as input to a multilayer perceptron neural network for building the model for initial-climb aircraft-mass prediction. Thus, the proposed method offers the advantages of both the model-based and data-driven approaches for aircraft-mass estimation. Because this method does not explicitly rely on any aircraft or engine parameter, it is universally applicable to all aircraft types. In this study, the proposed method was applied to a set of Boeing 777-300ER aircrafts, the results of which demonstrated reasonable accuracy. Airlines can use this tool to better utilize aircraft's payload.