A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem
出版年份 2021 全文链接
标题
A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem
作者
关键词
Rolling element bearings, Intelligent fault diagnosis, Transfer learning, Dynamic model, Convolutional neural network, Small sample
出版物
ISA TRANSACTIONS
Volume -, Issue -, Pages -
出版商
Elsevier BV
发表日期
2021-04-05
DOI
10.1016/j.isatra.2021.03.042
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Advance spectral approach for condition evaluation of rolling element bearings
- (2020) Prashant Tiwari et al. ISA TRANSACTIONS
- A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions
- (2020) Tianhao Hu et al. MEASUREMENT
- Applications of machine learning to machine fault diagnosis: A review and roadmap
- (2020) Yaguo Lei et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery
- (2020) Xiang Li et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Fault detection and identification of rolling element bearings with Attentive Dense CNN
- (2020) Spyridon Plakias et al. NEUROCOMPUTING
- Few-shot transfer learning for intelligent fault diagnosis of machine
- (2020) Jingyao Wu et al. MEASUREMENT
- Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions
- (2019) Te Han et al. ISA TRANSACTIONS
- A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network
- (2019) Yuantao Yang et al. ISA TRANSACTIONS
- Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions
- (2019) Md Junayed Hasan et al. MEASUREMENT
- A novel gas turbine fault diagnosis method based on transfer learning with CNN
- (2019) Shi-sheng Zhong et al. MEASUREMENT
- An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings
- (2019) Bin Yang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis
- (2019) Dengyu Xiao et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
- Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks
- (2019) Gaowei Xu et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification
- (2018) Jun Pan et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Mechanical model development of rolling bearing-rotor systems: A review
- (2018) Hongrui Cao et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Simulation-driven machine learning: Bearing fault classification
- (2018) Cameron Sobie et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Artificial intelligence for fault diagnosis of rotating machinery: A review
- (2018) Ruonan Liu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A review on the application of deep learning in system health management
- (2018) Samir Khan et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
- (2018) Wei Zhang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition
- (2018) Zirui Wang et al. NEUROCOMPUTING
- A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning
- (2018) Xiang Li et al. NEUROCOMPUTING
- Deep visual domain adaptation: A survey
- (2018) Mei Wang et al. NEUROCOMPUTING
- Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning
- (2018) Pei Cao et al. IEEE Access
- Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning
- (2018) Siyu Shao et al. IEEE Transactions on Industrial Informatics
- A survey on Deep Learning based bearing fault diagnosis
- (2018) Duy-Tang Hoang et al. NEUROCOMPUTING
- Multi-Layer domain adaptation method for rolling bearing fault diagnosis
- (2018) Xiang Li et al. SIGNAL PROCESSING
- A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets
- (2018) Jinhai Liu et al. IEEE Transactions on Industrial Informatics
- Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings
- (2017) Yongbo Li et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Ball bearing defect models: A study of simulated and experimental fault signatures
- (2017) C. Mishra et al. JOURNAL OF SOUND AND VIBRATION
- Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions
- (2017) Ran Zhang et al. IEEE Access
- An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
- (2016) Yaguo Lei et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
- (2016) Xiaojie Guo et al. MEASUREMENT
- Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications
- (2016) Yanxue Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Data-Based Techniques Focused on Modern Industry: An Overview
- (2015) Shen Yin et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches
- (2015) Zhiwei Gao et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- A nonlinear dynamic vibration model of defective bearings – The importance of modelling the finite size of rolling elements
- (2015) Alireza Moazen Ahmadi et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Transfer Learning for Visual Categorization: A Survey
- (2015) Ling Shao et al. IEEE Transactions on Neural Networks and Learning Systems
- Dynamic Modeling and Vibration Response Simulation for High Speed Rolling Ball Bearings With Localized Surface Defects in Raceways
- (2014) Linkai Niu et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
- Analyses of contact forces and vibration response for a defective rolling element bearing using an explicit dynamics finite element model
- (2014) Sarabjeet Singh et al. JOURNAL OF SOUND AND VIBRATION
- From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
- (2013) Xuewu Dai et al. IEEE Transactions on Industrial Informatics
- Transfer learning for activity recognition: a survey
- (2013) Diane Cook et al. KNOWLEDGE AND INFORMATION SYSTEMS
- LIBSVM
- (2012) Chih-Chung Chang et al. ACM Transactions on Intelligent Systems and Technology
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Simulating gear and bearing interactions in the presence of faults
- (2007) N. Sawalhi et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started