Related references
Note: Only part of the references are listed.Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing
Taeyun Kim et al.
SENSORS (2021)
Adversarial Multiple-Target Domain Adaptation for Fault Classification
Mohamed Ragab et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)
Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation
Xiang Li et al.
NEUROCOMPUTING (2020)
Generative Adversarial Networks
Ian Goodfellow et al.
COMMUNICATIONS OF THE ACM (2020)
Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation
Xiaodong Wang et al.
SENSORS (2020)
Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery
Zhuyun Chen et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
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)
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)
Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement
Lucia Frosini et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)
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)
Share Paper
