Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks
The turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation, which affects the reliability and performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the ke...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/11/3/67 |
| _version_ | 1797473455058190336 |
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| author | Unnati Thakkar Hicham Chaoui |
| author_facet | Unnati Thakkar Hicham Chaoui |
| author_sort | Unnati Thakkar |
| collection | DOAJ |
| description | The turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation, which affects the reliability and performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the key. This research uses machine learning to provide a prediction framework for an aircraft’s remaining useful life (RUL) based on the entire life cycle data and deterioration parameter data (ML). For the engine’s lifetime assessment, a Deep Layer Recurrent Neural Network (DL-RNN) model is presented. The suggested method is compared to Multilayer Perceptron (MLP), Nonlinear Auto Regressive Network with Exogenous Inputs (NARX), and Cascade Forward Neural Network (CFNN), as well as the Prognostics and Health Management (PHM) conference Challenge dataset and NASA’s C-MAPSS dataset. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are calculated for both the datasets, and the values are in the range of 0.15% to 0.203% for DL-RNN, whereas for the other three topologies, they are in the range of 0.2% to 4.8%. Comparative results show a better predictive accuracy with respect to other ML algorithms. |
| first_indexed | 2024-03-09T20:14:44Z |
| format | Article |
| id | doaj.art-95d9acda5442422aaa2ad8726f35034a |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-09T20:14:44Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-95d9acda5442422aaa2ad8726f35034a2023-11-24T00:04:27ZengMDPI AGActuators2076-08252022-02-011136710.3390/act11030067Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural NetworksUnnati Thakkar0Hicham Chaoui1Intelligent Robotic and Energy Systems Research Group, Faculty of Engineering and Design, Carleton University, Ottawa, ON K1S 5B6, CanadaIntelligent Robotic and Energy Systems Research Group, Faculty of Engineering and Design, Carleton University, Ottawa, ON K1S 5B6, CanadaThe turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation, which affects the reliability and performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the key. This research uses machine learning to provide a prediction framework for an aircraft’s remaining useful life (RUL) based on the entire life cycle data and deterioration parameter data (ML). For the engine’s lifetime assessment, a Deep Layer Recurrent Neural Network (DL-RNN) model is presented. The suggested method is compared to Multilayer Perceptron (MLP), Nonlinear Auto Regressive Network with Exogenous Inputs (NARX), and Cascade Forward Neural Network (CFNN), as well as the Prognostics and Health Management (PHM) conference Challenge dataset and NASA’s C-MAPSS dataset. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are calculated for both the datasets, and the values are in the range of 0.15% to 0.203% for DL-RNN, whereas for the other three topologies, they are in the range of 0.2% to 4.8%. Comparative results show a better predictive accuracy with respect to other ML algorithms.https://www.mdpi.com/2076-0825/11/3/67neural networksprognostics and health managementremaining useful lifedeep layer recurrent neural networkhealth indicatoraircraft engine |
| spellingShingle | Unnati Thakkar Hicham Chaoui Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks Actuators neural networks prognostics and health management remaining useful life deep layer recurrent neural network health indicator aircraft engine |
| title | Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks |
| title_full | Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks |
| title_fullStr | Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks |
| title_full_unstemmed | Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks |
| title_short | Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks |
| title_sort | remaining useful life prediction of an aircraft turbofan engine using deep layer recurrent neural networks |
| topic | neural networks prognostics and health management remaining useful life deep layer recurrent neural network health indicator aircraft engine |
| url | https://www.mdpi.com/2076-0825/11/3/67 |
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