Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network
Authors:
- Zhuyun ChenZCZhuyun Chen
- Jingyan XiaJXJingyan Xia
- Jipu LiJLJipu Li
- Junbin ChenJCJunbin Chen
- Ruyi HuangRuyi Huang
- Gang JinGJGang Jin
- Weihua LiWLWeihua Li
Top Cited Papers
- 13 June 2023
- journal article
- research article
- Published by Elsevier in Advanced Engineering Informatics
- Vol. 57
Abstract
No abstract availableKeywords
Funding Information
- National Key Research and Development Program of China (2018YFB1702400)
- Basic and Applied Basic Research Foundation of Guangdong Province (2021A1515110708, 202201010615)
- Beijing Information Science and Technology University (KF20212223204)
- National Natural Science Foundation of China (51875208, 52205100, 52205101, 52275111)
This publication has 35 references indexed in Scilit:
- A Robust Weight-Shared Capsule Network for Intelligent Machinery Fault DiagnosisIEEE Transactions on Industrial Informatics, 2020
- Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary ConditionsIEEE Transactions on Industrial Informatics, 2019
- Universal Domain Adaptation2019
- Multi-Layer domain adaptation method for rolling bearing fault diagnosisSignal Processing, 2019
- A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faultsKnowledge-Based Systems, 2019
- Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural NetworksIEEE Access, 2018
- Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled DataIEEE Transactions on Industrial Electronics, 2018
- Maximum Classifier Discrepancy for Unsupervised Domain Adaptation2018
- Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machineKnowledge-Based Systems, 2018
- A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration SignalsSensors, 2017