Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion
Aeroengine is one of the most concerned objects of the relevant aviation industry and researchers, and it is a hard work to assess and predict performance degradation due to the complex structure and the changeable operating condition of the engine. In order to realize the performance degradation as...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5876299 |
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| _version_ | 1832560516543807488 |
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| author | Hongsheng Yan Hongfu Zuo Jianzhong Sun Di Zhou Han Wang |
| author_facet | Hongsheng Yan Hongfu Zuo Jianzhong Sun Di Zhou Han Wang |
| author_sort | Hongsheng Yan |
| collection | DOAJ |
| description | Aeroengine is one of the most concerned objects of the relevant aviation industry and researchers, and it is a hard work to assess and predict performance degradation due to the complex structure and the changeable operating condition of the engine. In order to realize the performance degradation assessment and remaining useful life (RUL) prediction of aeroengine, this paper proposes a two-stage assessment and prediction method based on data fusion. First, the standard deviation merged by multiple selected features is used as the health indicator to characterize the engine performance. Second, a sliding window detection method called average local window slope is proposed to determine the current health state of observations by a specified rule. Finally, the RUL prediction is performed on the observation in the two stages, respectively. On the one hand, a similarity-based RUL prediction method is used to engines in the health stage, and on the other hand, for engines in the degradation stage, a RUL prediction method based on a mapping function of the standard deviation and the current using cycle is established. The proposed method has been applied and verified on the NASA’s C-MAPSS simulation data. Results of degradation assessment and prediction show that the proposed method is trustworthy and feasible from the engineering perspective, and it has better performance in the comprehensive indicator compared with other methods. |
| format | Article |
| id | doaj-art-46939c8039cb455687d488858bc6be2d |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-46939c8039cb455687d488858bc6be2d2025-02-03T01:27:20ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742021-01-01202110.1155/2021/58762995876299Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data FusionHongsheng Yan0Hongfu Zuo1Jianzhong Sun2Di Zhou3Han Wang4Key Laboratory of Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province 211106, ChinaKey Laboratory of Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province 211106, ChinaKey Laboratory of Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province 211106, ChinaKey Laboratory of Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province 211106, ChinaKey Laboratory of Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province 211106, ChinaAeroengine is one of the most concerned objects of the relevant aviation industry and researchers, and it is a hard work to assess and predict performance degradation due to the complex structure and the changeable operating condition of the engine. In order to realize the performance degradation assessment and remaining useful life (RUL) prediction of aeroengine, this paper proposes a two-stage assessment and prediction method based on data fusion. First, the standard deviation merged by multiple selected features is used as the health indicator to characterize the engine performance. Second, a sliding window detection method called average local window slope is proposed to determine the current health state of observations by a specified rule. Finally, the RUL prediction is performed on the observation in the two stages, respectively. On the one hand, a similarity-based RUL prediction method is used to engines in the health stage, and on the other hand, for engines in the degradation stage, a RUL prediction method based on a mapping function of the standard deviation and the current using cycle is established. The proposed method has been applied and verified on the NASA’s C-MAPSS simulation data. Results of degradation assessment and prediction show that the proposed method is trustworthy and feasible from the engineering perspective, and it has better performance in the comprehensive indicator compared with other methods.http://dx.doi.org/10.1155/2021/5876299 |
| spellingShingle | Hongsheng Yan Hongfu Zuo Jianzhong Sun Di Zhou Han Wang Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion International Journal of Aerospace Engineering |
| title | Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion |
| title_full | Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion |
| title_fullStr | Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion |
| title_full_unstemmed | Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion |
| title_short | Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion |
| title_sort | two stage degradation assessment and prediction method for aircraft engine based on data fusion |
| url | http://dx.doi.org/10.1155/2021/5876299 |
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