A Machine Learning Approach to Forecasting Turbofan Engine Health Using Real Flight Data

Silva, Fernanda C. da; Grinet, Marco A. V. M.; Silva, André R.

doi:10.2514/6.2022-0491

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Fang et al proposed a way to extend the acceleration controller of a small bypass ratio turbofan engine based on deep reinforcement learning to tackle the multidimensional constraint optimization issue in continuous action space [22]. Advanced machine learning methods, such as Cond-LSTM, were found to give precise predictions for turbofan engine performance under specific flight conditions by Silva et al [23].…”

Section: Introductionmentioning

confidence: 99%

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Ahmed,

Sohail,

Javed

et al. 2023

Applied Sciences

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Parametric cycle analysis, an on-design engine study, specifies the required design characteristics that optimize engine performance. This study aimed to conduct a parametric analysis of a low-bypass turbofan engine with an afterburner, F100-PW229, and develop a technique for estimating its performance based on data using machine learning and deep learning. Commercially available gas turbine simulation software, GasTurb 14, was used to create a dataset of engine performance response variables and input design parameters. The effects of the Mach number, fan pressure ratio, altitude, turbine entry temperature, and bypass ratio on the specific thrust, propulsive efficiency, specific fuel consumption, and total fuel flow were investigated. Regression learning models and deep neural networks were then programmed on this dataset to predict responses for new input data. In MATLAB, a total of 24 regression models were trained with cross-validation, and the model with the least root mean square error was selected as the final model. The machine learning regression models produced reliable output parameter predictions, with the least root mean square error of 9.076 × 10−5. Among the numerous regression models tested, Gaussian process regression, the quadratic support vector machine, and the wide neural network emerged to be the most successful in predicting turbofan engine performance metrics. A multilayer perceptron model was coded in Python with two hidden layers that accurately predicted the performance parameters. The mean square error value on test data was found to be as low as 0.0046. In comparison to intensive computational simulations, machine learning and deep learning models offer an efficient method for conducting parametric analysis of turbofan engines.

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Section: Introductionmentioning

confidence: 99%

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Ahmed,

Sohail,

Javed

et al. 2023

Applied Sciences

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Turbofan engine health status prediction with neural network pattern recognition and automated feature engineering

Szrama

2024

AEAT

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Purpose This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE). Design/methodology/approach The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE). Findings The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE. Practical implications This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention. Originality/value Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

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Studies of Erosive Wear of the Blading in Axial Compressors of Gas Turbines (Review)

et al. 2023

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A Machine Learning Approach to Forecasting Turbofan Engine Health Using Real Flight Data

Cited by 3 publications

References 15 publications

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Turbofan engine health status prediction with neural network pattern recognition and automated feature engineering

Studies of Erosive Wear of the Blading in Axial Compressors of Gas Turbines (Review)

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