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所属单位：民航学院

发表刊物：Aerosp Sci Technol

摘要：The development of airborne sensor monitoring and artificial intelligence technologies provides effective tools for precise prognostic and health management (PHM) of aircraft. This paper presents a PHM model which combines multiple deep learning algorithms for condition assessment, fault classification, sensor prediction, and remaining useful life (RUL) estimation of aircraft systems. A long short-term memory (LSTM) based recurrent network is used to predict multiple multivariate time series of sensors, and deep belief network (DBN) is applied to assess system condition and classify faults of aircraft systems. Then, the RUL can be estimated through the integration of condition assessment and sensor prediction. Finally, the proposed algorithm is validated experimentally using NASA's C-MAPSS dataset, and the results showed a lower error rate and deviation than traditional models. © 2019 Elsevier Masson SAS

ISSN号：1270-9638

是否译文：否

发表时间：2019-11-01

合写作者：Che, Changchang,Fu, Qiang,吴富强,Ni, Xiaomei

通讯作者：Che, Changchang,王华伟