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所属单位：民航学院

发表刊物：Zhendong Ceshi Yu Zhenduan

摘要：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-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. © 2017, Editorial Department of JVMD. All right reserved.

ISSN号：1004-6801

是否译文：否

发表时间：2017-02-01

合写作者：张建,李艳军

通讯作者：曹愈远