Oversampling adversarial network for class-imbalanced fault diagnosis

A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis

(2020) Zhijun Ren et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis

(2020) Chenyu Liu et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

(2020) Zhuyun Chen et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine

(2019) Zhuyun Chen et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

G-GANISR: Gradual generative adversarial network for image super resolution

(2019) Pourya Shamsolmoali et al.   NEUROCOMPUTING

Sparse Deep Stacking Network for Fault Diagnosis of Motor

(2018) Chuang Sun et al.   IEEE Transactions on Industrial Informatics

Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

(2018) Min Xia et al.   IEEE-ASME TRANSACTIONS ON MECHATRONICS

Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization

(2018) Feng Jia 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 neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

(2018) Feng Jia et al.   NEUROCOMPUTING

Transfer Boosting With Synthetic Instances for Class Imbalanced Object Recognition

(2018) Xuesong Zhang et al.   IEEE Transactions on Cybernetics

An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics

(2018) Zhenyu Wu et al.   IEEE Access

Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks

(2018) Han Liu et al.   NEUROCOMPUTING

An Integrated Class-Imbalanced Learning Scheme for Diagnosing Bearing Defects in Induction Motors

(2017) Roozbeh Razavi-Far et al.   IEEE Transactions on Industrial Informatics

Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

(2017) Wentao Mao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Kernelized support vector machine with deep learning: An efficient approach for extreme multiclass dataset

(2017) Masoumeh Zareapoor et al.   PATTERN RECOGNITION LETTERS

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

(2017) Wei Zhang et al.   SENSORS

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

(2017) Chen Lu et al.   SIGNAL PROCESSING

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

(2016) Yaguo Lei et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Convolutional Neural Network Based Fault Detection for Rotating Machinery

(2016) Olivier Janssens et al.   JOURNAL OF SOUND AND VIBRATION

Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier

(2013) Changqing Shen et al.   MEASUREMENT

A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox

(2011) Bing Li et al.   EXPERT SYSTEMS WITH APPLICATIONS