An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation-Reference-Cited by-同舟云学术

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An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation

Published:2020-02 Issue: Volume:115 Page:103182
ISSN:0166-3615
Container-title:Computers in Industry
language:en
Short-container-title:Computers in Industry

Author:

Xia Tangbin,Song Ya,Zheng Yu,Pan Ershun,Xi Lifeng

Funder

National Natural Science Foundation of China

State Key Lab of Mechanical System and Vibration program

Intelligent Manufacturing Industrial Projects of Lingang Area

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference49 articles.

1. A multimodal and hybrid deep neural network model for remaining useful life estimation;Al-Dulaimi;Comput. Ind.,2019

2. Deep neural network for traffic sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods;Arcos-Garcia;Neural Netw.,2018

3. A neural network filtering approach for similarity-based remaining useful life estimation;Bektas;Int. J. Adv. Manuf. Technol.,2019

4. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches;Bouktif;Energies,2018

5. A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing;Cui;Measurement,2019

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