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。Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults
Bin Yang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)
Federated Forest
Yang Liu et al.
IEEE TRANSACTIONS ON BIG DATA (2022)
Reliable Federated Learning for Mobile Networks
Jiawen Kang et al.
IEEE WIRELESS COMMUNICATIONS (2020)
A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes
Huipeng Chen et al.
MEASUREMENT (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)
A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals
Tianyou Chen et al.
MEASUREMENT (2019)
The impact of class imbalance in classification performance metrics based on the binary confusion matrix
Amalia Luque et al.
PATTERN RECOGNITION (2019)
An improved deep network for intelligent diagnosis of machinery faults with similar features
Jing Yang et al.
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING (2019)
A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
Meidi Sun et al.
MEASUREMENT (2019)
Deep residual learning-based fault diagnosis method for rotating machinery
Wei Zhang et al.
ISA TRANSACTIONS (2019)
Deep transfer network for rotating machine fault analysis
Weiwei Qian et al.
PATTERN RECOGNITION (2019)
Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions
Changchang Che et al.
ADVANCES IN MECHANICAL ENGINEERING (2019)
Federated learning of predictive models from federated Electronic Health Records
Theodora S. Brisimi et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2018)
Improved similarity-based modeling for the classification of rotating-machine failures
Matheus A. Marins et al.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (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)
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)
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
Luyang Jing et al.
MEASUREMENT (2017)
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Shao Haidong et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
Convolutional neural networks for hyperspectral image classification
Shiqi Yu et al.
NEUROCOMPUTING (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)
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
Meng Gan et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)
Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
Feng Jia et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (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)
Failure diagnosis using deep belief learning based health state classification
Prasanna Tamilselvan et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2013)