A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events
This research proposes a novel hybrid reliability analysis method for rare failure events, which integrates the coupled Adaptive Kriging model and Generalized Subset Simulation (AK-GSS). In the proposed method, the adaptive Kriging model is applied to approximate the actual Performance Function (PF)...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10721413/ |
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| _version_ | 1850195041743536128 |
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| author | Yunhan Ling Huajun Peng Yong Sun Chao Yuan Zining Su Xiaoxiao Tian Peng Nie Hengfei Yang Shiyuan Yang |
| author_facet | Yunhan Ling Huajun Peng Yong Sun Chao Yuan Zining Su Xiaoxiao Tian Peng Nie Hengfei Yang Shiyuan Yang |
| author_sort | Yunhan Ling |
| collection | DOAJ |
| description | This research proposes a novel hybrid reliability analysis method for rare failure events, which integrates the coupled Adaptive Kriging model and Generalized Subset Simulation (AK-GSS). In the proposed method, the adaptive Kriging model is applied to approximate the actual Performance Function (PF) to reduce the number of PF calls. A newly updated strategy is proposed to look for samples on the limit state surface to achieve active learning of the Kriging model. This updated strategy avoids the limitations of most current learning functions based on the prediction variance of Kriging models. The advantages of AK-GSS are illustrated through five examples, including two engineering applications of aircraft wings and hydraulic turbine rotor brackets. The results show that the proposed method is more efficient and accurate for rare failure events. |
| format | Article |
| id | doaj-art-894320cfb0894e5db692e3a5b4d566c2 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-894320cfb0894e5db692e3a5b4d566c22025-08-20T02:13:52ZengIEEEIEEE Access2169-35362024-01-011216362116363710.1109/ACCESS.2024.348356710721413A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure EventsYunhan Ling0Huajun Peng1Yong Sun2Chao Yuan3Zining Su4Xiaoxiao Tian5https://orcid.org/0009-0001-8096-5990Peng Nie6Hengfei Yang7Shiyuan Yang8https://orcid.org/0000-0003-4255-440XChina Academy of Machinery Beijing Research Institute of Mechanical and Electrical Technology Co., Ltd., Beijing, ChinaAVIC Guizhou Anda Aviation Forging Company Ltd., Anshun, Guizhou, ChinaChina Academy of Machinery Beijing Research Institute of Mechanical and Electrical Technology Co., Ltd., Beijing, ChinaChina Academy of Machinery Beijing Research Institute of Mechanical and Electrical Technology Co., Ltd., Beijing, ChinaChina Academy of Machinery Beijing Research Institute of Mechanical and Electrical Technology Co., Ltd., Beijing, ChinaChina Academy of Machinery Beijing Research Institute of Mechanical and Electrical Technology Co., Ltd., Beijing, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaThis research proposes a novel hybrid reliability analysis method for rare failure events, which integrates the coupled Adaptive Kriging model and Generalized Subset Simulation (AK-GSS). In the proposed method, the adaptive Kriging model is applied to approximate the actual Performance Function (PF) to reduce the number of PF calls. A newly updated strategy is proposed to look for samples on the limit state surface to achieve active learning of the Kriging model. This updated strategy avoids the limitations of most current learning functions based on the prediction variance of Kriging models. The advantages of AK-GSS are illustrated through five examples, including two engineering applications of aircraft wings and hydraulic turbine rotor brackets. The results show that the proposed method is more efficient and accurate for rare failure events.https://ieeexplore.ieee.org/document/10721413/Generalized subset simulationhybrid reliability analysiskriging modelrare failure events |
| spellingShingle | Yunhan Ling Huajun Peng Yong Sun Chao Yuan Zining Su Xiaoxiao Tian Peng Nie Hengfei Yang Shiyuan Yang A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events IEEE Access Generalized subset simulation hybrid reliability analysis kriging model rare failure events |
| title | A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events |
| title_full | A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events |
| title_fullStr | A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events |
| title_full_unstemmed | A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events |
| title_short | A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events |
| title_sort | coupled adaptive kriging model and generalized subset simulation hybrid reliability analysis method for rare failure events |
| topic | Generalized subset simulation hybrid reliability analysis kriging model rare failure events |
| url | https://ieeexplore.ieee.org/document/10721413/ |
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