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。Damage detection in a novel deep-learning framework: a robust method for feature extraction
Tian Guo et al.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)
Hierarchical hyper-Laplacian prior for weak fault feature enhancement
Zhibin Zhao et al.
ISA TRANSACTIONS (2020)
Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
He Zhiyi et al.
KNOWLEDGE-BASED SYSTEMS (2020)
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Biao Wang et al.
IEEE TRANSACTIONS ON RELIABILITY (2020)
A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis
Zhijun Ren et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
Bo Zhao et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Zhibin Zhao et al.
ISA TRANSACTIONS (2020)
A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis
Long Wen et al.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2019)
A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals
Huaqing Wang et al.
COMPUTERS IN INDUSTRY (2019)
Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
Chuang Sun et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings
Bin Yang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
Liang Guo et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Deep learning and its applications to machine health monitoring
Rui Zhao et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Domain Adaptive Motor Fault Diagnosis Using Deep Transfer Learning
Dengyu Xiao et al.
IEEE ACCESS (2019)
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
Salman H. Khan et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)
Additive Margin Softmax for Face Verification
Feng Wang et al.
IEEE SIGNAL PROCESSING LETTERS (2018)
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
Long Wen et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
Haidong Shao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes
Minghang Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
Feng Jia et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Artificial intelligence for fault diagnosis of rotating machinery: A review
Ruonan Liu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
A review on the application of deep learning in system health management
Samir Khan et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
Wei Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Yaguo Lei et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Deep Model Based Domain Adaptation for Fault Diagnosis
Weining Lu et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2017)
An Integrated Class-Imbalanced Learning Scheme for Diagnosing Bearing Defects in Induction Motors
Roozbeh Razavi-Far et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2017)
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
Osama Abdeljaber et al.
JOURNAL OF SOUND AND VIBRATION (2017)
Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine
Wentao Mao et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Shao Haidong et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
Wei Zhang et al.
SENSORS (2017)
SphereFace: Deep Hypersphere Embedding for Face Recognition
Weiyang Liu et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Convolutional Neural Network Based Fault Detection for Rotating Machinery
Olivier Janssens et al.
JOURNAL OF SOUND AND VIBRATION (2016)
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
Wade A. Smith et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2015)
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Yaguo Lei et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2013)
A Survey on Transfer Learning
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)
Rolling element bearing fault diagnosis using wavelet packets
NG Nikolaou et al.
NDT & E INTERNATIONAL (2002)
分享论文
