A5087489952- Machine Fault Diagnosis Techniques
- stochastic dynamics and bifurcation
- Probabilistic and Robust Engineering Design
- Fault Detection and Control Systems
- Gear and Bearing Dynamics Analysis
- Diffusion and Search Dynamics
- Vibration and Dynamic Analysis
- Metallurgy and Material Forming
- Ecosystem dynamics and resilience
- Engineering Diagnostics and Reliability
- Energy Load and Power Forecasting
- Structural Health Monitoring Techniques
- EEG and Brain-Computer Interfaces
- Advanced Measurement and Detection Methods
- Iterative Learning Control Systems
- Force Microscopy Techniques and Applications
- Neural dynamics and brain function
- Advanced Fiber Laser Technologies
- Advanced Algorithms and Applications
- Anomaly Detection Techniques and Applications
- Non-Destructive Testing Techniques
- Optical Network Technologies
- Blind Source Separation Techniques
- Mineral Processing and Grinding
- Structural Integrity and Reliability Analysis
Yanshan University
2015-2024
Huawei Technologies (China)
2021
Georgia Institute of Technology
2018
Institute of Electrical Engineering
2016
Beijing University of Posts and Telecommunications
2009-2011
Shanghai Jiao Tong University
2002-2004
Compared to the single-source domain adaptation fault diagnosis methods, multi-source methods can not only take advantage of rich and diverse diagnostic information multiple source domains but also draw on feature alignment setting reduce discrepancy. However, forcing distributions is challenging may lead negative transfer. Meanwhile, labeled data are often scarce difficult collect in actual production, which be mitigated by information, performance model degraded large differences. To...
Abstract With the wide application of wind turbines, bearing fault diagnosis turbines has become a research hotspot. Under complex variable working conditions, vibration signals components show non-stationary characteristics. Therefore, it is challenging to extract features using typical methods. This paper proposes Adaptive Multivariate Variational Mode Decomposition combined with an improved Deep Discrimination Transfer Learning Network (AMVMD-IDDTLN) for under conditions. First, AMVMD...