Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Authors:
- Jian LinJLJian Lin
- Haidong ShaoHSHaidong Shao
- Xiangdong ZhouXZXiangdong Zhou
- Baoping CaiBCBaoping Cai
- Bin LiuBLBin Liu
Top Cited Papers
- 8 June 2023
- journal article
- research article
- Published by Elsevier in Expert Systems with Applications
- Vol. 230
Abstract
No abstract availableKeywords
This publication has 34 references indexed in Scilit:
- Few-shot transfer learning for intelligent fault diagnosis of machineMeasurement, 2020
- Intelligent fault diagnosis of mechanical equipment under varying working condition via iterative matching network augmented with selective Signal reuse strategyJournal of Manufacturing Systems, 2020
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks2020
- Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samplesKnowledge-Based Systems, 2020
- Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer LearningIEEE Transactions on Industrial Informatics, 2019
- Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearingMeasurement, 2019
- Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural NetworksIEEE Transactions on Industrial Electronics, 2018
- Learning to Compare: Relation Network for Few-Shot Learning2018
- A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration SignalsSensors, 2017
- Classification of hyperspectral remote sensing images with support vector machinesIEEE Transactions on Geoscience and Remote Sensing, 2004