| Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System |
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| 作者姓名： | LI Jie SHEN Shi-tuan |
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| 作者单位： | LI Jie*，SHEN Shi-tuan School of Electronics and Information Engineering，Beijing University of Aeronautics and Astronautics，Beijing 100083，China |
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| 摘 要： | Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic element is the test-diagnosis fault unit. Every underlying fault cause’s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples, the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.
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| 关 键 词： | 故障询问系统 航空控制系统 模糊专家系统 故障诊断系统 |
| 收稿时间： | 7 July 2006 |
| 修稿时间： | 2006-07-072006-10-13 |
Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System |
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| LI Jie SHEN Shi-tuan.Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System[J].Chinese Journal of Aeronautics,2007,20(3):223-229. |
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| Authors: | Jie LI Shi-tuan SHEN |
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| Institution: | aSchool of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China |
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| Abstract: | Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic element is the test-diagnosis fault unit. Every underlying fault cause's membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples,the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle. |
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| Keywords: | fuzzy expert system fault query network fault answer best selection algorithm fuzzy theory test-diagnosis fault unit Fuzzy Expert System Based Fault Diagnosis Device Algorithm capability synthesis diagnosis improvement principle examples deep knowledge best selection membership degree fuzzy product inference |
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