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所属单位：民航学院

发表刊物：Beijing Hangkong Hangtian Daxue Xuebao

摘要：Through the fault diagnosis of aero-engine, the working status of each component can be correctly judged, and the maintenance program can be determined quickly to ensure the safety of flight. Based on the combination of deep belief network and decision fusion theory, the fault fusion diagnosis model of aero-engine based on deep learning was proposed. This model, through analyzing a large number of engine performance parameters, starts with getting fault classification confidence via hidden features in engine performance parameters extracted by deep learning algorithm, and then the multiple fault classification results were fused by decision fusion method to get more accurate results. The JT9D engine failure coefficient was simulated as data to prove the validity of the method. The results of an example show that the reliability of the data has been improved by fault fusion diagnosis of several experimental results, and the model has high fault classification and diagnosis accuracy and anti-interference ability. © 2018, Editorial Board of JBUAA. All right reserved.

ISSN号：1001-5965

是否译文：否

发表时间：2018-03-01

合写作者：Che, Changchang,Ni, Xiaomei,Hong, Jiyu

通讯作者：王华伟