Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems
The complex multi-state systems joint operation simulation and two task strategies are explored. For Monte Carlo sampling, based on the indirect sampling method, the principle of accelerated sampling methods (ASM), including forced transition (FT) and fault biasing (FB), is studied. It is found that...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9780402/ |
| _version_ | 1798042061204619264 |
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| author | Zihao Xiong Zongren Xie Yifan Xu Jianwei Lv |
| author_facet | Zihao Xiong Zongren Xie Yifan Xu Jianwei Lv |
| author_sort | Zihao Xiong |
| collection | DOAJ |
| description | The complex multi-state systems joint operation simulation and two task strategies are explored. For Monte Carlo sampling, based on the indirect sampling method, the principle of accelerated sampling methods (ASM), including forced transition (FT) and fault biasing (FB), is studied. It is found that the direct adoption of the current FT and FB will result in systematic bias for the calculation of task success metrics. For the bias, a state-transition chain truncation rule (SCTR) is proposed. In case study, after the simulation of various parameter combinations, it is found that when the sailing time is relatively smaller than the equipment reliability, which means a small probability event for the sampling of equipment faults, the ASM based on indirect sampling is applicable. Otherwise, the conventional sampling method should be used. The proposed state-transition chain truncation rule is also verified in case study. |
| first_indexed | 2024-04-11T22:30:15Z |
| format | Article |
| id | doaj.art-600984c688174ce2b6b9871fcdc66dad |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:30:15Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-600984c688174ce2b6b9871fcdc66dad2022-12-22T03:59:26ZengIEEEIEEE Access2169-35362022-01-0110855578557010.1109/ACCESS.2022.31776149780402Accelerated Sampling Research Under Task Optimized Strategy for Multistate SystemsZihao Xiong0https://orcid.org/0000-0003-0146-2007Zongren Xie1Yifan Xu2https://orcid.org/0000-0002-9021-3653Jianwei Lv3Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan, ChinaStrategic Assessment and Consulting Center, Academy of Military Sciences, Beijing, ChinaDepartment of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan, ChinaDepartment of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan, ChinaThe complex multi-state systems joint operation simulation and two task strategies are explored. For Monte Carlo sampling, based on the indirect sampling method, the principle of accelerated sampling methods (ASM), including forced transition (FT) and fault biasing (FB), is studied. It is found that the direct adoption of the current FT and FB will result in systematic bias for the calculation of task success metrics. For the bias, a state-transition chain truncation rule (SCTR) is proposed. In case study, after the simulation of various parameter combinations, it is found that when the sailing time is relatively smaller than the equipment reliability, which means a small probability event for the sampling of equipment faults, the ASM based on indirect sampling is applicable. Otherwise, the conventional sampling method should be used. The proposed state-transition chain truncation rule is also verified in case study.https://ieeexplore.ieee.org/document/9780402/Multi-state systemssailing strategiesMonte Carlo simulationASMSCTRapplication rules |
| spellingShingle | Zihao Xiong Zongren Xie Yifan Xu Jianwei Lv Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems IEEE Access Multi-state systems sailing strategies Monte Carlo simulation ASM SCTR application rules |
| title | Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems |
| title_full | Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems |
| title_fullStr | Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems |
| title_full_unstemmed | Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems |
| title_short | Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems |
| title_sort | accelerated sampling research under task optimized strategy for multistate systems |
| topic | Multi-state systems sailing strategies Monte Carlo simulation ASM SCTR application rules |
| url | https://ieeexplore.ieee.org/document/9780402/ |
| work_keys_str_mv | AT zihaoxiong acceleratedsamplingresearchundertaskoptimizedstrategyformultistatesystems AT zongrenxie acceleratedsamplingresearchundertaskoptimizedstrategyformultistatesystems AT yifanxu acceleratedsamplingresearchundertaskoptimizedstrategyformultistatesystems AT jianweilv acceleratedsamplingresearchundertaskoptimizedstrategyformultistatesystems |