| 基于新型机器学习的电子装备系统智能故障诊断研究 |
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| 引用本文： | 杨云,朱家元,张恒喜.基于新型机器学习的电子装备系统智能故障诊断研究[J].计算机工程与应用,2003,39(22):210-211,232. |
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| 作者姓名： | 杨云 朱家元 张恒喜 |
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| 作者单位： | 1. 北京航空航天大学工程系统工程系,北京,100083
2. 空军工程大学工程学院飞机与发动机工程系,西安,710038 |
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| 基金项目： | 国家部委预研基金，部级重点工程资助 |
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| 摘 要： | 支持向量机是一种基于结构风险最小原则的新型机器学习方法,具有完备的理论依据和良好的学习泛化能力。该文针对电子装备系统特征,采用支持向量机算法构建智能故障诊断模型,并对典型电子设备进行故障诊断。结果表明,该诊断模型是可行的、有效的,具有一定工程应用价值。
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| 关 键 词： | 神经网络 机器学习 支持向量机 电子装备 故障诊断 |
| 文章编号： | 1002-8331-(2003)22-0210-02 |
Electronic Equipment Systems Intelligent Fault Diagnosis Based on New Machine Learning Approach |
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| Yang Yun,Zhu Jiayuan,Zhang Hengxi.Electronic Equipment Systems Intelligent Fault Diagnosis Based on New Machine Learning Approach[J].Computer Engineering and Applications,2003,39(22):210-211,232. |
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| Authors: | Yang Yun Zhu Jiayuan Zhang Hengxi |
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| Affiliation: | Yang Yun 1 Zhu Jiayuan 2 Zhang Hengxi 21 |
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| Abstract: | Support vector machines is a new statistical learning method based on structural risk minimization principle,and it has integrated theory and valid learning generalization ability.This paper presents a new electronic equipment sys-tems intelligent fault diagnosis mode based on support vector machines.And then uses it to diagnose for typical equip-ment.The results show that the diagnosis mode is valid and rational.And also it improves the accuracy of fault diagnosis comparing to traditional methods and has great engineering application value. |
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| Keywords: | Neural networks Machine learning Support vector machines Electronic equipment Fault diagnosis |
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