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Parametric estimation scheme for aircraft fuel consumption using machine learning

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Abstract

The most efficient technique that is used for aircraft engine tuning is through mounting the engine on the engine test bench (ETB) to analyze, tune and monitor its variables through the ETB run. It is practically very difficult to unmount the engine from the aircraft and mount it on the ETB for analyzing and estimating a single variable such as fuel consumption or oil temperature as the unmounting process requires huge manpower and machinery. This problem can be resolved if the fuel consumption of an air vehicle is estimated without unmounting the engine from the aircraft through applying data analytics and machine learning models. Therefore, in this paper, the fuel consumption of an aircraft is analyzed and estimated through advanced data science techniques. The dataset went through data analyzing and preprocessing techniques before applying multiple machine learning models such as multiple linear regression (MLR), support vector regression, decision tree regression and deep learning algorithm RNN/LSTM. The performance of algorithms has been evaluated using model evaluation methods such as mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination. The models are evaluated in taxi, cruise and approach flight phases where the LSTM performs excellent among all other algorithms with RMSE 15.1%, 10.5% and 0.9%, respectively.

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Data availability

The data used in this research is sourced from the Honeywell dataset, which is publicly available. The dataset can be accessed via ref.  [42].

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Authors and Affiliations

  1. Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada

    Mirza Anas Wahid

  2. Department of Electrical and Computer Engineering, Air University, Islamabad, Pakistan

    Syed Hashim Raza Bukhari

  3. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan

    Muazzam Maqsood & Farhan Aadil

  4. Department of Electrical Engineering, City University of HongKong, Kowloon, Hong Kong

    Muhammad Ismail Khan

  5. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan

    Muhammad Ismail Khan & Saeed Ehsan Awan

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  1. Mirza Anas Wahid
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Correspondence to Syed Hashim Raza Bukhari.

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Wahid, M.A., Bukhari, S.H.R., Maqsood, M. et al. Parametric estimation scheme for aircraft fuel consumption using machine learning. Neural Comput & Applic 35, 24925–24946 (2023). https://doi.org/10.1007/s00521-023-08981-4

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