Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN
With the deepening adoption of whole-lifecycle management paradigms in aviation equipment, accurate prediction of remaining useful life has emerged as a pivotal technical enabler for ensuring flight safety and optimizing maintenance resource allocation. This study systematically addresses limitation...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/583 |
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| author | Lihua Shen Yucheng Wang Baorui Du Hailong Yang He Fan |
| author_facet | Lihua Shen Yucheng Wang Baorui Du Hailong Yang He Fan |
| author_sort | Lihua Shen |
| collection | DOAJ |
| description | With the deepening adoption of whole-lifecycle management paradigms in aviation equipment, accurate prediction of remaining useful life has emerged as a pivotal technical enabler for ensuring flight safety and optimizing maintenance resource allocation. This study systematically addresses limitations in existing data-driven remaining useful life prediction methodologies through comprehensive optimizations spanning data preprocessing protocols and model architectural enhancements. To mitigate the local optimum entrapment inherent in deep learning hyperparameter optimization, an improved Gray Wolf Optimizer incorporating dynamic perturbation factors is proposed. This algorithm is subsequently deployed to optimize hyperparameters within the redesigned predictive architecture. Comparative analyses reveal that the proposed framework achieves superior prediction accuracy compared to mainstream optimization-driven models and state-of-the-art approaches. The results substantiate the capability of dynamic perturbation strategies to enhance both hyperparameter optimization quality and prediction stability, ultimately delivering an efficient solution for aero-engine remaining useful life estimation. The experiments on the C-MAPSS Dataset verify the effectiveness of these improvements. |
| format | Article |
| id | doaj-art-a4820b4f6de54d78bffa2a40b01ae0b4 |
| institution | Matheson Library |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-a4820b4f6de54d78bffa2a40b01ae0b42025-07-25T13:28:37ZengMDPI AGMachines2075-17022025-07-0113758310.3390/machines13070583Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNNLihua Shen0Yucheng Wang1Baorui Du2Hailong Yang3He Fan4College of Mechanical and Electrical Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaCollege of Mechanical and Electrical Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaThe Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100094, ChinaThe Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Mechanical and Electrical Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaWith the deepening adoption of whole-lifecycle management paradigms in aviation equipment, accurate prediction of remaining useful life has emerged as a pivotal technical enabler for ensuring flight safety and optimizing maintenance resource allocation. This study systematically addresses limitations in existing data-driven remaining useful life prediction methodologies through comprehensive optimizations spanning data preprocessing protocols and model architectural enhancements. To mitigate the local optimum entrapment inherent in deep learning hyperparameter optimization, an improved Gray Wolf Optimizer incorporating dynamic perturbation factors is proposed. This algorithm is subsequently deployed to optimize hyperparameters within the redesigned predictive architecture. Comparative analyses reveal that the proposed framework achieves superior prediction accuracy compared to mainstream optimization-driven models and state-of-the-art approaches. The results substantiate the capability of dynamic perturbation strategies to enhance both hyperparameter optimization quality and prediction stability, ultimately delivering an efficient solution for aero-engine remaining useful life estimation. The experiments on the C-MAPSS Dataset verify the effectiveness of these improvements.https://www.mdpi.com/2075-1702/13/7/583aero-engineremaining useful life predictiongray wolf optimizerconvolutional neural network |
| spellingShingle | Lihua Shen Yucheng Wang Baorui Du Hailong Yang He Fan Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN Machines aero-engine remaining useful life prediction gray wolf optimizer convolutional neural network |
| title | Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN |
| title_full | Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN |
| title_fullStr | Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN |
| title_full_unstemmed | Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN |
| title_short | Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN |
| title_sort | remaining useful life prediction of aero engine based on improved gwo and 1dcnn |
| topic | aero-engine remaining useful life prediction gray wolf optimizer convolutional neural network |
| url | https://www.mdpi.com/2075-1702/13/7/583 |
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