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论文类型：期刊论文

发表时间：2022-06-29

发表刊物：Journal of Computer Applications

卷号：40

期号：12

页面范围：3534-3540

ISSN号：1001-9081

关键字："data-driven model; Remaining Useful Life(RUL)prediction; Temporal Convolutional Network(TCN); ensembling method; turbofan engine"

CN号：51-1307/TP

摘要：As the turbofan engine is one of the core equipment in the field of aerospace,its health condition determines whether the aircraft could work stably and reliably.And the prediction of the Remaining Useful Life(RUL)of turbofan engine is an important part of equipment monitoring and maintenance.In view of the characteristics such as complicated operating conditions,diverse monitoring data,and long time span existing in the turbofan engine monitoring process,a remaining useful life prediction model for turbofan engines integrating Genetic Algorithm-based Selective ENsembling(GASEN)and Temporal Convolutional Network(TCN)(GASEN-TCN)was proposed.Firstly,TCN was used to capture the inner relationship between data under long span,so as to predict the RUL.Then,GASEN was applied to ensemble multiple independent TCNs for enhancing the generalization performance of the model.Finally,the proposed model was compared with the popular machine learning methods and other deep neural networks on the general Commercial Modular Aero-Propulsion System Simulation(C-MAPSS)dataset.Experimental results show that,the proposed model has higher prediction accuracy and lower prediction error than the state-of-the-art Bidirectional Long-Short Term Memory(Bi-LSTM)network under many different operating modes and fault conditions.Taking FD001 dataset as an example:on this dataset,the Root Mean Square Error(RMSE)of the proposed model is 17.08% lower than that of Bi-LSTM,and the relative accuracy(Accuracy)of the proposed model is 12.16% higher than that of Bi-LSTM.It can be seen that the proposed model has considerable application prospect in intelligent overhaul and maintenance of equipment.

备注：新增回溯数据