Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
As the core component of an airplane, the health status of the aviation engine is crucial for the safe operation of the aircraft. Therefore, predicting the remaining service life of the engine is of great significance for ensuring its safety and reliability. In this paper, a multichannel hybrid netw...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/966 |
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| author | Yonghao He Changjun Wen Wei Xu |
| author_facet | Yonghao He Changjun Wen Wei Xu |
| author_sort | Yonghao He |
| collection | DOAJ |
| description | As the core component of an airplane, the health status of the aviation engine is crucial for the safe operation of the aircraft. Therefore, predicting the remaining service life of the engine is of great significance for ensuring its safety and reliability. In this paper, a multichannel hybrid network is proposed; this network is a combination of the one-dimensional convolutional neural network (1D-CNN), the bidirectional long short-term memory network (BiLSTM), and the self-attention mechanism. For each sensor of the engine, an SA-CNN-BiLSTM network is established. The one-dimensional convolutional neural network and the bidirectional long short-term memory network are used to extract the spatial features and temporal features of the input data, respectively. Moreover, multichannel modeling is utilized to achieve the parallel processing of different sensors. Subsequently, the results are stitched together to establish a mapping relationship with the engine’s remaining useful life (RUL). Experimental validation was conducted on the aero-engine C-MAPSS dataset. The prediction results were compared with those of the other seven models to verify the effectiveness of this method in predicting the remaining service life. The results indicate that the proposed method significantly reduces the prediction error compared to other models. Specifically, for the two datasets, their mean absolute errors were only 11.47 and 11.76, the root-mean-square error values were only 12.26 and 12.78, and the scoring function values were only 195 and 227. |
| format | Article |
| id | doaj-art-a15b191e68b345818a57fd8250a7c0a0 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a15b191e68b345818a57fd8250a7c0a02025-01-24T13:21:31ZengMDPI AGApplied Sciences2076-34172025-01-0115296610.3390/app15020966Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid NetworkYonghao He0Changjun Wen1Wei Xu2School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaAs the core component of an airplane, the health status of the aviation engine is crucial for the safe operation of the aircraft. Therefore, predicting the remaining service life of the engine is of great significance for ensuring its safety and reliability. In this paper, a multichannel hybrid network is proposed; this network is a combination of the one-dimensional convolutional neural network (1D-CNN), the bidirectional long short-term memory network (BiLSTM), and the self-attention mechanism. For each sensor of the engine, an SA-CNN-BiLSTM network is established. The one-dimensional convolutional neural network and the bidirectional long short-term memory network are used to extract the spatial features and temporal features of the input data, respectively. Moreover, multichannel modeling is utilized to achieve the parallel processing of different sensors. Subsequently, the results are stitched together to establish a mapping relationship with the engine’s remaining useful life (RUL). Experimental validation was conducted on the aero-engine C-MAPSS dataset. The prediction results were compared with those of the other seven models to verify the effectiveness of this method in predicting the remaining service life. The results indicate that the proposed method significantly reduces the prediction error compared to other models. Specifically, for the two datasets, their mean absolute errors were only 11.47 and 11.76, the root-mean-square error values were only 12.26 and 12.78, and the scoring function values were only 195 and 227.https://www.mdpi.com/2076-3417/15/2/966convolutional neural networkbidirectional long short-term memory networkattention mechanismsresidual life prediction |
| spellingShingle | Yonghao He Changjun Wen Wei Xu Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network Applied Sciences convolutional neural network bidirectional long short-term memory network attention mechanisms residual life prediction |
| title | Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network |
| title_full | Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network |
| title_fullStr | Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network |
| title_full_unstemmed | Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network |
| title_short | Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network |
| title_sort | residual life prediction of sa cnn bilstm aero engine based on a multichannel hybrid network |
| topic | convolutional neural network bidirectional long short-term memory network attention mechanisms residual life prediction |
| url | https://www.mdpi.com/2076-3417/15/2/966 |
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