Prediction of remaining useful life of turbofan engine based on optimized model

To realize the prognostics and health management (PHM) of the mechanical system, it is the key to accurately predict the remaining useful life (RUL) of the equipment. The network captured features at different time steps will contribute to the final RUL prediction to varying degrees. Therefore, a de...

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Bibliographic Details
Main Authors: Liu, Yuefeng, Zhang, Xiaoyan, Guo, Wei, Bian, Haodong, He, Yingjie, Liu, Zhen
Format: Conference Proceeding
Language:English
Subjects:
attention mechanism
Data privacy
Deep learning
Feature extraction
Predictive models
prognostics and health management
remaining useful life
Representation learning
Security
Solids
turbofan engine
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Summary:To realize the prognostics and health management (PHM) of the mechanical system, it is the key to accurately predict the remaining useful life (RUL) of the equipment. The network captured features at different time steps will contribute to the final RUL prediction to varying degrees. Therefore, a deep learning network based on the attention mechanism is proposed. Firstly, the raw sensor data is passed to the Bi-LSTM network to capture the long-term dependence of features. Secondly, the features extracted by Bi-LSTM are passed to the attention mechanism for feature weighting, thereby giving greater weight to important features. Finally, the weighted features are input into the fully connected network to predict the RUL of the turbofan engine. Using the data set C-MAPSS to explore the feasibility of this method. The results show that this method is more accurate than other RUL prediction methods.
ISSN:2324-9013
DOI:10.1109/TrustCom53373.2021.00210