2018•elib (German Aerospace Center)Open access

Evaluation of Aircraft Performance Variation during Daily Flight Operations

Christoph Deiler

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Abstract

A novel energybased methodology to predict the flight performance within an entire aircraft fleet based on flight data records is presented. It combines knowledge about the aircrafts flight mechanics with statistical methods and estimation techniques to solve the big data problem. Therefore, this method provides a new smart way to analyze the flight data of modern aircraft during daily flight operations (normal airline operation) to monitor the change of individual aircraft characteristics. The methodology is described in detail first, validated with simulated flight data afterwards and finally applied to flight data of more than 75 000 flights with a Boeing B737 fleet. The corresponding very promising results show that this distinct knowledgebased methodology allows to reliably predict the aircraft flight performance variation and can easily overcome several shortcomings such as poor data resolution or limited quality.

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What this paper is about

A novel energybased methodology to predict the flight performance within an entire aircraft fleet based on flight data records is presented. It combines knowledge about the aircrafts flight mechanics with statistical methods and estimation techniques to solve the big data problem. Therefore, this method provides a new smart way to analyze the flight data of modern aircraft during daily flight operations (normal airline operation) to monitor the change of individual aircraft characteristics. The methodology is described in detail first, validated with simulated flight data afterwards and finally applied to flight data of more than 75 000 flights with a Boeing B737 fleet. The corresponding very promising results show that this distinct knowledgebased methodology allows to reliably predict the aircraft flight performance variation and can easily overcome several shortcomings such as poor data resolution or limited quality.

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Available abstract

A novel energybased methodology to predict the flight performance within an entire aircraft fleet based on flight data records is presented. It combines knowledge about the aircrafts flight mechanics with statistical methods and estimation techniques to solve the big data problem. Therefore, this method provides a new smart way to analyze the flight data of modern aircraft during daily flight operations (normal airline operation) to monitor the change of individual aircraft characteristics. The methodology is described in detail first, validated with simulated flight data afterwards and finally applied to flight data of more than 75 000 flights with a Boeing B737 fleet. The corresponding very promising results show that this distinct knowledgebased methodology allows to reliably predict the aircraft flight performance variation and can easily overcome several shortcomings such as poor data resolution or limited quality.

Key concepts: Variation (astronomy), Aircraft flight mechanics, Flight simulator, Aeronautics, Fly-by-wire, Computer science, Airplane, Flight plan

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