Zhuyun Chen

Orcid: 0000-0002-6100-7332

Affiliations:
  • South China University of Technology, School of Mechanical and Automotive Engineering, Guangzhou, China
  • Guangdong Artificial Intelligence and Digital Economy Laboratory, Pazhou Lab, Guangzhou, China


According to our database1, Zhuyun Chen authored at least 38 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Knowledge Embedded Autoencoder Network for Harmonic Drive Fault Diagnosis Under Few-Shot Industrial Scenarios.
IEEE Internet Things J., July, 2024

A novel weakly supervised adversarial network for thermal error modeling of electric spindles with scarce samples.
Expert Syst. Appl., March, 2024

Digital Twin-Assisted Fault Diagnosis of Rotating Machinery Without Measured Fault Data.
IEEE Trans. Instrum. Meas., 2024

Time-Frequency RWGAN for Machine Anomaly Detection Under Varying Working Conditions.
IEEE Trans. Instrum. Meas., 2024

A relationship-aware calibrated prototypical network for fault incremental diagnosis of electric motors without reserved samples.
Reliab. Eng. Syst. Saf., 2024

Interpretable multi-task neural network modeling and particle swarm optimization of process parameters in laser welding.
Knowl. Based Syst., 2024

An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery.
Expert Syst. Appl., 2024

A digital twin-driven approach for partial domain fault diagnosis of rotating machinery.
Eng. Appl. Artif. Intell., 2024

Dynamic characteristics modeling and optimization for hydraulic engine mounts based on deep neural network coupled with genetic algorithm.
Eng. Appl. Artif. Intell., 2024

Fault diagnosis of gearbox driven by vibration response mechanism and enhanced unsupervised domain adaptation.
Adv. Eng. Informatics, 2024

Fault Prior-guided White-box Model towards Interpretable Discriminant Feature Extraction.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2024

Source-free Open-set Domain Adaptation Network for Emerging Fault Diagnosis of Planetary Gearbox.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2024

An Interpretable Fault Diagnosis Framework based on Capsule Network with Statistical Features.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2024

Multi-Scale Dilated Convolutional Auto-Encoder Network for Weak Feature Extraction and Health Condition Detection.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2024

2023
A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis.
Reliab. Eng. Syst. Saf., December, 2023

Improved Vibration Signal Models of Localized Faults of Sun Gears to Predict Modulation.
Symmetry, September, 2023

Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices.
Sensors, September, 2023

Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network.
Adv. Eng. Informatics, August, 2023

Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression.
Reliab. Eng. Syst. Saf., June, 2023

A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery.
IEEE Trans. Cybern., March, 2023

Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions.
Adv. Eng. Informatics, January, 2023

Multiple Source-Free Domain Adaptation Network Based on Knowledge Distillation for Machinery Fault Diagnosis.
IEEE Trans. Instrum. Meas., 2023

A LightGBM-Based Multiscale Weighted Ensemble Model for Few-Shot Fault Diagnosis.
IEEE Trans. Instrum. Meas., 2023

Gradient flow-based meta generative adversarial network for data augmentation in fault diagnosis.
Appl. Soft Comput., 2023

Resource-Efficient Network for Intelligent Fault Diagnosis Using Tree-Structured Parzen Estimator.
Proceedings of the 7th International Conference on System Reliability and Safety, 2023

Gradient-Based Interpretable Graph Convolutional Network for Bearing Fault Diagnosis.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2023

2022
A Reweighted Overlapping Group Shrinkage Method for Bearing Fault Diagnosis.
IEEE Trans. Instrum. Meas., 2022

Deep Self-Supervised Domain Adaptation Network for Fault Diagnosis of Rotating Machine With Unlabeled Data.
IEEE Trans. Instrum. Meas., 2022

Dual-Attention-Based Multiscale Convolutional Neural Network With Stage Division for Remaining Useful Life Prediction of Rolling Bearings.
IEEE Trans. Instrum. Meas., 2022

Federated Transfer Learning for Bearing Fault Diagnosis With Discrepancy-Based Weighted Federated Averaging.
IEEE Trans. Instrum. Meas., 2022

2021
A Novel Weighted Adversarial Transfer Network for Partial Domain Fault Diagnosis of Machinery.
IEEE Trans. Ind. Informatics, 2021

A Global-Local Dynamic Adversarial Network for Intelligent Fault Diagnosis of Spindle Bearing.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2021

2020
Deep Semisupervised Domain Generalization Network for Rotary Machinery Fault Diagnosis Under Variable Speed.
IEEE Trans. Instrum. Meas., 2020

Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery.
IEEE Trans. Instrum. Meas., 2020

Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network.
IEEE Trans. Ind. Informatics, 2020

2019
Gearbox Fault Diagnosis Using Convolutional Neural Networks And Support Vector Machines.
Proceedings of the 27th European Signal Processing Conference, 2019

2017
Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network.
IEEE Trans. Instrum. Meas., 2017

2016
Machine fault classification using deep belief network.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2016


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