Rolling bearing fault diagnosis using optimal ensemble deep transfer network
Authors:
- Xingqiu LiXLXingqiu Li
- Hongkai JiangHJHongkai Jiang
- Ruixin WangRWRuixin Wang
- Maogui NiuMNMaogui Niu
Top Cited Papers
- 1 February 2021
- journal article
- research article
- Published by Elsevier in Knowledge-Based Systems
- Vol. 213
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (91860124)
- National Natural Science Foundation of China (51875459)
- Synergy Innovation Foundation of the University and Enterprise for Graduate Students in Northwestern Polytechnical University (XQ201901)
This publication has 34 references indexed in Scilit:
- Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualizationMechanical Systems and Signal Processing, 2018
- Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer LearningIEEE Access, 2018
- Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensingMechanical Systems and Signal Processing, 2018
- Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory DataIEEE Transactions on Industrial Informatics, 2018
- Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary GearboxesIEEE Transactions on Industrial Electronics, 2017
- Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machinesMechanical Systems and Signal Processing, 2017
- Transfer learning using computational intelligence: A surveyKnowledge-Based Systems, 2015
- Transfer Feature Learning with Joint Distribution Adaptation2013
- Domain Adaptation via Transfer Component AnalysisIEEE Transactions on Neural Networks, 2010
- A Survey on Transfer LearningIEEE Transactions on Knowledge and Data Engineering, 2009