%0 Journal Article
%T 基于模糊粗糙集和SVM的航空发动机故障诊断<br>Aero-engine Fault Diagnosis Based on Fuzzy Rough Set and SVM
%A 曹愈远
%A 张建
%A 李艳军
%A 张丽娜
%J 振动.测试与诊断
%D 2017
%R 10.16450/j.cnki.issn.1004-6801.2017.01.027
%X 着航空产业的发展，航空发动机故障诊断逐渐向智能化、精确化方向发展，针对这一趋势结合模糊聚类、粗糙集以及支持向量机理论，提出了一种航空发动机故障诊断方法。首先，运用模糊C 均值聚类算法将连续数据离散化；然后，运用粗糙集的知识发现理论，在保持决策表的决策属性和条件属性之间的依赖关系不发生变化的前提下对决策表进行约简；最后，利用支持向量机适用于小样本数据处理的特性对样本进行学习得到最优超平面决策函数从而进行故障诊断。对航空发动机性能参数实例的验证结果表明，该方法对航空发动机故障具有较强的诊断能力，在不影响诊断率的基础上大大缩短了运算时间。因此，提出的算法具有较好的实用性和准确性。<br>With the development of the aviation industry, methods for aero-engine fault diagnosis have become increasingly intelligent and accurate. In this paper, we proposed a method that combines fuzzy clustering, rough sets and support vector machine (SVM). First, a fuzzy C-average clustering algorithm was applied to discretize the continuous data. Then, we used the knowledge discovery theory of rough set to reduce the decision table under the premise of keeping the table‘s attribute and dependencies between conditions attributes unchanged. We used the SVM to study samples to obtain the optimal hyperげplane decision function. Finally, we used the diagnosis faults based on these characteristics for the data processing of small samples. The instance validation results of the aero-engine performance parameters showed that our method had improved ability to diagnosis aero-engine faults and could greatly shorten operation time without affecting the diagnostic rate. Thus, the proposed algorithm is both practical and accurate.
%K 航空发动机
%K 故障诊断
%K 模糊聚类
%K 粗糙集
%K 支持向量机&lt
%K br&gt
%K Aero-engine
%K fault diagnosis
%K fuzzy clustering
%K rough set
%K support vector machine
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201701027&flag=1