Analysis of Virtual Sensors for Predicting Aircraft Fuel Consumption

Metadata Updated: July 17, 2020

Previous research described the use of machine learning algorithms to predict aircraft fuel consumption. This technique, known as Virtual Sensors, models fuel consumption as a function of aircraft Flight Operations Quality Assurance (FOQA) data. FOQA data consist of a large number of measurements that are already recorded by many commercial airlines. The predictive model is used for anomaly detection in the fuel consumption history by noting when measured fuel consumption exceeds an expected value. This exceedance may indicate overconsumption of fuel, the source of which may be identified and corrected by the aircraft operator. This would reduce both fuel emissions and operational costs. This paper gives a brief overview of the modeling approach and describes efforts to validate and analyze the initial results of this project. We examine the typical error in modeling, and compare modeling accuracy against both complex and simplistic regression approaches. We also estimate a ranking of the importance of each FOQA variable used as input, and demonstrate that FOQA variables can reliably be used to identify different modes of fuel consumption, which may be useful in future work. Analysis indicates that fuel consumption is accurately predicted while remaining theoretically sensitive to sub-nominal pilot inputs and maintenance-related issues.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
Publisher Dashlink
Unique Identifier DASHLINK_620
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 98987bbc-2b36-46f3-b1a8-2404a139d566
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2012-10-02
Homepage URL
Data Last Modified 2020-01-29
Program Code 026:029
Source Datajson Identifier True
Source Hash 1c5a0289334fbf5868c71db49acbd34dff7b5e8f
Source Schema Version 1.1

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