A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery-Reference-Cited by-同舟云学术

A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery

Published:2018-06-22 Issue:6 Volume:34 Page:3513-3521
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Jiang Hongkai¹,Shao Haidong¹,Chen Xinxia²,Huang Jiayang²

Affiliation:

1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, People’s Republic of China

2. Shanghai Engineering Research Center of Civil Aircraft Monitoring, Shanghai, People’s Republic of China

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference20 articles.

1. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis;Jiang;Mechanical Systems and Signal Processing,2013

2. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet;Shao;ISA Transactions,2017

3. Wavelet transform based on inner product in fault diagnosis of rotating machinery: Areview;Chen;Mechanical Systems and Signal Processing,2016

4. Automatic Feature extraction of time-seriesapplied to fault severity evaluation of helical gearbox instationary and non-stationary speed operation;Cabrera;Applied SoftComputing,2017

5. A multidimensional hybrid intelligent method for gear fault diagnosis;Lei;Expert Systems with Applications,2010

Cited by 32 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. Fault Diagnosis of Rolling Element Bearing from Vibration Signal Using One Dimensional Convolutional Neural Networks;2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE);2024-06-19

2. Research on deep-learning-based fault diagnosis and prediction methods for electrical systems;International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023);2024-02-28

3. Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks;Computer Modeling in Engineering & Sciences;2024

4. A novel HB-SC-MCCNN model for intelligent fault diagnosis of rolling bearing;Journal of Mechanical Science and Technology;2023-12

5. Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm;Measurement Science and Technology;2023-11-01