Engine Fault Diagnosis Method Based on Convolution Neural Network and BiLSTM-Reference-Cited by-同舟云学术

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Engine Fault Diagnosis Method Based on Convolution Neural Network and BiLSTM

Published:2023-04-14 Issue: Volume:34 Page:156-162
ISSN:
Container-title:2023 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)
language:
Short-container-title:

Author:

Feng Donghua¹,Li Yahong²,Wu Sa³

Affiliation:

1. Software and Communication School, Tianjin Sino-German University of Applied Sciences,Tianjin,China

2. School of Information Engineering Tianjin College, Beijing University of Science and Technology,Tianjin,China

3. University of Newcastle upon Tyne,Faculty of Science, Agricultuer and Engineering,Newcaslt Upon Tyne,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10412666/10412708/10412767.pdf?arnumber=10412767

Reference12 articles.

1. Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

2. Vaidyanathan. Hybrid CNN-LSTM for Traffic Flow Forecasting;Rajalakshmi

3. Fault Diagnosis of Civil Aviation Engine Driven Based on DBN;Shisheng;Journal of Aerospace Power,2019

4. Fault diagnosis approach for diesel engines based on IMS clustering algorithm;Xiaobo;Journal of Vibration and shock,2018

5. Feature extraction and prediction of QAR data based on CNN-LSTM;Peng;Application Research of Computers,2019

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