A Comparative Analysis of Exhaust Gas Temperature Based on Machine Learning Models for Aviation Applications

Atasoy, Vehbi Emrah; Suzer, Ahmet Esat; Ekici, Selçuk

doi:10.1115/1.4052771

Cited by 25 publications

(8 citation statements)

References 51 publications

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“…Engine EGT is affected by many factors. According to the literature [36,37], the parameters related to EGT extracted from ACARS data are listed in Table 1, including H, TAT, Ma, N1, N2 and Wf. Since they are the main parameters that affect the change of EGT, these factors were used as input parameters of the baseline model, and the EGTOEM was used as the output target of the model.…”

Section: Establishment Of the Baseline Modelmentioning

confidence: 99%

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

Wang

Zhao

2022

Aerospace

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The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.

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Section: Establishment Of the Baseline Modelmentioning

confidence: 99%

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

Wang

Zhao

2022

Aerospace

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“…As in many sectors, fuel rates in the aviation industry constitute a significant part of operational costs. Therefore, airlines, aircraft manufacturers, scientists and air traffic authorities develop and implement special procedures to reduce fuel consumption and emissions (Atasoy et al , 2021; Dinc, 2017; Dinc and Otkur, 2021; Ligrani et al , 2017; Mark and Selwyn, 2016; Yazar et al , 2017). İn addition, they develop and test different fuel mixtures in new and innovative studies in the literature.…”

Section: Introductionmentioning

confidence: 99%

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

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Purpose The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended. Design/methodology/approach The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM. Findings In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R2, root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R2, RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R2, RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained. Originality/value In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.

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“…They expressed that the exergy efficiency of the engine is predicted as R 2 = 0.9974 with LSM and R 2 = 0.9999 with GA‐LSM. Atasoy et al 19 studied for prediction of EGT of turbofan engines employing deep learning and support vector machine (SVM). The authors stated that the EGT model obtained by deep learning has higher correctness with R 2 = 0.9834 compared with that obtained by the SVM method, which has R 2 = 0.9712.…”

Section: Introductionmentioning

confidence: 99%

Modeling of performance and thermodynamic metrics of a conceptual turboprop engine by comparing different machine learning approaches

Dursun

Toraman

Aygün

2022

Intl J of Energy Research

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In this study, several performance and thermodynamics metrics of a conceptual turboprop engine (C-TPE) were computed at 50 (fifty) power settings. Based on these computations, these parameters were predicted by employing artificial neural networks (ANN) and long-short term memory (LSTM) approaches. The obtained parametrically data were subjected to preprocessing for normalization. After determining model inputs to estimate the engine outputs, these data were introduced to ANN and LSTM. Then, the findings by two methods were compared with each other. In this context, fuel flow, air mass flow, exhaust velocity, compressor pressure ratio, turbine outlet temperature, and revolutions per minute (RPM) were selected as model inputs to estimate several metrics such as net thrust, specific fuel consumption (SFC), overall efficiency, exergy efficiency, and environmental effect factor (EEF) regarding the C-TPE. For modeling of the mentioned metrics, 80% and 20% of the data were used for training and testing, respectively. According to the findings of energetic and exergetic computations, SFC value of the C-TPE increases from 16.205 to 17.737 g/ kNs whereas its overall efficiency varies from 23.63% to 22.31% owing to decrement in RPM. Moreover, exergy efficiency of C-TPE remains between 23.93% and 26.2% whilst its EFF value resides between 2.66 and 3.05 throughout power settings. Finally, the modeling results indicate that coefficient of determination (R 2 ) regarding overall efficiency of C-TPE was found as 0.951867 with respect to the ANN model whereas it was calculated as 0.999906 with the proposed LSTM. It could be deduced that since LSTM is capable of providing lower the model error than the ANN for the same set of data, this method could be implemented to predict thermodynamics metrics of different kinds of gas turbine engine where obtaining data is limited.

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A Comparative Analysis of Exhaust Gas Temperature Based on Machine Learning Models for Aviation Applications

Cited by 25 publications

References 51 publications

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Modeling of performance and thermodynamic metrics of a conceptual turboprop engine by comparing different machine learning approaches

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