Fault detection in rotary agricultural machinery using genetic algorithm optimized multiple input – parallel – convolutional neural networks-Reference-Cited by-同舟云学术

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Fault detection in rotary agricultural machinery using genetic algorithm optimized multiple input – parallel – convolutional neural networks

Published:2023-11-05 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 International Conference on Electrical Engineering and Advanced Technology (ICEEAT)
language:
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Author:

Sewioło Mateusz¹,Mystkowski Arkadiusz¹,Berghout Tarek²,Khamari Dalila²,Wolszczak Piotr³,Litak Grzegorz³

Affiliation:

1. Bialystok University of Technology,Faculty of Electrical Engineering,Bialystok,Poland,15-351

2. University of Batna 2,Faculty of Technology,Batna,Algeria,05078

3. Lublin University of Technology,Faculty of Mechanical Engineering,Lublin,Poland,20-618

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10424967/10425818/10425835.pdf?arnumber=10425835

Reference15 articles.

1. Methods of Condition Monitoring and Fault Detection for Electrical Machines

2. Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input

3. Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

4. Condition Monitoring using Convolutional Neural Network in Agricultural Machinery - Use Case: Disc Mower

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Agriculture Rotary Tedder Fault Diagnosis using Multiple Input - Multiple Type - Parallel - Convolutional Neural Network Optimized by Genetic Algorithm;2024 25th International Carpathian Control Conference (ICCC);2024-05-22

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