摘要
为了解决复杂电路系统故障样本少、特征信息冗杂的问题,提出了一种基于粗糙集属性约简理论和支持向量机分类方法相结合的故障诊断方法。首先采用粗糙集约简故障模式库中的冗余特征属性和矛盾样本,然后提取最简故障特征模式作为支持向量机的学习样本,通过样本训练使构建的支持向量机多分类器能够快速实现故障诊断的目的。最后,通过仿真算例验证了该方法在小样本故障识别上的有效性和可行性。
In complex circuit system, it is lack of fault samples and the feature information is miscellaneous and redundant. To solve the problem, a new fault diagnosis method was presented based on Rough Set （RS） and Support Vector Machine （SVM）. The RS was applied to reduce the redundant attributes and the conflicting samples. Then the simplest fault attributes were extracted as the training samples for SVM, which was used as the classifier to isolate the faults rapidly. The simulated experiments demonstrated that the method is valid and feasible under the condition of small samples.
出处
《电光与控制》
北大核心
2009年第3期83-85,94,共4页
Electronics Optics & Control
基金
国家自然科学基金重点课题(60736026)
国家教育部新世纪优秀人才支持计划项目
关键词
故障诊断
粗糙集
支持向量机
复杂系统
fault diagnosis
rough set
Support Vector Machine（SVM）
complex system
