【摘 要】
：
针对机械故障诊断中存在的小样本、模糊、不确定性特征数据等问题,将改进三角模糊理论与支持向量分类机(SVM)方法相结合,提出一种Fv-SVM模型,给出相应的机械故障诊断方法.最
【机 构】
：
东南大学江苏省微纳生物医疗器械设计与制造重点实验室,东南大学复杂工程系统测量与控制教育部重点实验室,郑州牧业工程高等专科学校,上海海事大学信息工程学院,
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针对机械故障诊断中存在的小样本、模糊、不确定性特征数据等问题,将改进三角模糊理论与支持向量分类机(SVM)方法相结合,提出一种Fv-SVM模型,给出相应的机械故障诊断方法.最后进行了复杂设备故障诊断的实例分析,结果表明基于Fv-SVM的机械故障诊断方法是有效和可行的.
Aiming at the problems of small sample, fuzzy and uncertain characteristic data in mechanical fault diagnosis, this paper proposes an improved Fv-SVM model by combining the improved triangular fuzzy theory with SVM. The corresponding mechanical Fault diagnosis method.Finally, the case analysis of complex equipment fault diagnosis is carried out and the results show that the method of mechanical fault diagnosis based on Fv-SVM is effective and feasible.
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