Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network-Reference-Cited by-同舟云学术

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Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network

Published:2022 Issue: Volume:10 Page:82156-82163
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Al-Khazraji Huthaifa¹^ORCID,Nasser Ahmed R.¹,Hasan Ahmed M.¹,Al Mhdawi Ammar K.²^ORCID,Al-Raweshidy Hamed³^ORCID,Humaidi Amjad J.¹

Affiliation:

1. Control and Systems Engineering Department, University of Technology, Baghdad, Iraq

2. Department of Computer Science, Edge Hill University, Ormskirk, U.K.

3. Department of Electrical and Electronic Engineering, Brunel University London, Uxbridge, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09815246.pdf?arnumber=9815246

Reference30 articles.

1. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

2. A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction

3. Deep convolutional neural network based regression approach for estimation of remaining useful life;babu;Proc Int Conf Database Syst Advanced Appl,2016

4. Long Short-Term Memory Network for Remaining Useful Life estimation

5. Remaining useful life estimation in prognostics using deep convolution neural networks

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