An aero-engine remaining useful life prediction model based on feature selection and the improved TCN
Inferring the remaining useful life (RUL) of an aero-engine based on complex data from aircraft sensors is one of the important issues to ensure flight safety. To this end, this paper is intended to propose a RUL prediction model based on the feature extraction method and the improved temporal convo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Franklin Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186324000148 |
| _version_ | 1797202963234553856 |
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| author | Wenting Zha Yunhong Ye |
| author_facet | Wenting Zha Yunhong Ye |
| author_sort | Wenting Zha |
| collection | DOAJ |
| description | Inferring the remaining useful life (RUL) of an aero-engine based on complex data from aircraft sensors is one of the important issues to ensure flight safety. To this end, this paper is intended to propose a RUL prediction model based on the feature extraction method and the improved temporal convolution network (TCN). First, the XGBoost (eXtreme Gradient Boosting) model is used to assess the importance of the data and filter the features base on the resulting correlation. Then, the RUL prediction model is constructed by paralleling TCN networks with different expansion rates, which expands the receptive field and further improves the information obtained by the network from the data. Moreover, the network is further optimized with dynamic hyperparameter search methods. Finally, through comparative experiments, the proposed prediction model is evaluated based on the turbofan aero-engine operation failure prediction benchmark dataset (CMAPSS). The experimental results show that by deleting some data with low correlation, the proposed model can achieve better prediction accuracy, which is superior to other mainstream models in the references. |
| first_indexed | 2024-04-24T08:11:47Z |
| format | Article |
| id | doaj.art-711f67cadff84c058041b1fbd19e5b1f |
| institution | Directory Open Access Journal |
| issn | 2773-1863 |
| language | English |
| last_indexed | 2024-04-24T08:11:47Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj.art-711f67cadff84c058041b1fbd19e5b1f2024-04-17T04:50:32ZengElsevierFranklin Open2773-18632024-03-016100083An aero-engine remaining useful life prediction model based on feature selection and the improved TCNWenting Zha0Yunhong Ye1Corresponding author.; China University of Mining and Technology-Beijing, Beijing 100083, ChinaChina University of Mining and Technology-Beijing, Beijing 100083, ChinaInferring the remaining useful life (RUL) of an aero-engine based on complex data from aircraft sensors is one of the important issues to ensure flight safety. To this end, this paper is intended to propose a RUL prediction model based on the feature extraction method and the improved temporal convolution network (TCN). First, the XGBoost (eXtreme Gradient Boosting) model is used to assess the importance of the data and filter the features base on the resulting correlation. Then, the RUL prediction model is constructed by paralleling TCN networks with different expansion rates, which expands the receptive field and further improves the information obtained by the network from the data. Moreover, the network is further optimized with dynamic hyperparameter search methods. Finally, through comparative experiments, the proposed prediction model is evaluated based on the turbofan aero-engine operation failure prediction benchmark dataset (CMAPSS). The experimental results show that by deleting some data with low correlation, the proposed model can achieve better prediction accuracy, which is superior to other mainstream models in the references.http://www.sciencedirect.com/science/article/pii/S2773186324000148CMAPSSDeep learningTCNTime seriesRemaining useful life |
| spellingShingle | Wenting Zha Yunhong Ye An aero-engine remaining useful life prediction model based on feature selection and the improved TCN Franklin Open CMAPSS Deep learning TCN Time series Remaining useful life |
| title | An aero-engine remaining useful life prediction model based on feature selection and the improved TCN |
| title_full | An aero-engine remaining useful life prediction model based on feature selection and the improved TCN |
| title_fullStr | An aero-engine remaining useful life prediction model based on feature selection and the improved TCN |
| title_full_unstemmed | An aero-engine remaining useful life prediction model based on feature selection and the improved TCN |
| title_short | An aero-engine remaining useful life prediction model based on feature selection and the improved TCN |
| title_sort | aero engine remaining useful life prediction model based on feature selection and the improved tcn |
| topic | CMAPSS Deep learning TCN Time series Remaining useful life |
| url | http://www.sciencedirect.com/science/article/pii/S2773186324000148 |
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