A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis

Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions

(2021) Baokun Han et al.   MEASUREMENT

Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial Networks

(2021) Yong Zhi Liu et al.   MEASUREMENT

Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning

(2021) Wei Zhang et al.   IEEE Transactions on Industrial Informatics

Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

(2020) Xiaodong Wang et al.   SENSORS

Applications of machine learning to machine fault diagnosis: A review and roadmap

(2020) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep Convolution Domain-adversarial Transfer Learning for Fault Diagnosis of Rolling Bearings

(2020) Feng Li et al.   MEASUREMENT

Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions

(2020) Qi Li et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A Comprehensive Survey on Transfer Learning

(2020) Fuzhen Zhuang et al.   PROCEEDINGS OF THE IEEE

Federated learning for machinery fault diagnosis with dynamic validation and self-supervision

(2020) Wei Zhang et al.   KNOWLEDGE-BASED SYSTEMS

Deep Subdomain Adaptation Network for Image Classification

(2020) Yongchun Zhu et al.   IEEE Transactions on Neural Networks and Learning Systems

How Generative Adversarial Networks and Their Variants Work

(2019) Yongjun Hong et al.   ACM COMPUTING SURVEYS

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

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

(2019) Yinsheng Chen et al.   Applied Sciences-Basel

Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application

(2019) Te Han et al.   ISA TRANSACTIONS

Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine

(2019) Chen et al.   SENSORS

Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

(2019) Xiang Li et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

(2018) Xiang Li et al.   SIGNAL PROCESSING

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

(2018) Liang Guo et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

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

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

(2017) Te Han et al.   TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING