A5082971634- Gear and Bearing Dynamics Analysis
- Machine Fault Diagnosis Techniques
- Advanced machining processes and optimization
- Tribology and Lubrication Engineering
- Fault Detection and Control Systems
- Vibration and Dynamic Analysis
- Dynamics and Control of Mechanical Systems
- Advanced Measurement and Metrology Techniques
- Distributed Control Multi-Agent Systems
- Advanced Algorithms and Applications
- Hydraulic and Pneumatic Systems
- Neural Networks Stability and Synchronization
- Acoustic Wave Phenomena Research
- Mechanical stress and fatigue analysis
- Adhesion, Friction, and Surface Interactions
- Iterative Learning Control Systems
- Mineral Processing and Grinding
- Structural Health Monitoring Techniques
- Composite Structure Analysis and Optimization
- Mechanical Failure Analysis and Simulation
- Engineering Diagnostics and Reliability
- Advanced Numerical Analysis Techniques
- Railway Engineering and Dynamics
- Elevator Systems and Control
- Noise Effects and Management
Shenyang Jianzhu University
2016-2025
Northeastern University
2009-2014
Subsurface mesoscale cracks exist widely in the outer ring of full ceramic ball bearings (FCBBs), which is a potential threat for stable operation related devices such as aero engines, food processing machinery, and artificial replacement hip joints. This paper establishes dynamic model subsurface FCBBs based on strain energy theory, influence different crack lengths running state analyzed. The existence regarded weakening stiffness coefficient, deterioration degree thereby quantified. It...
The digital twin of life-cycle rolling bearing is significant for its degradation performance analysis and condition prediction. To solve the problem which not reliable to arrange production cycle by predicting diagnostic results in existing studies, because it accurate only consider single-scale fault modeling. It studied that multiscale evolution law close true involves microscopic cracks, mesoscopic spall, macroscopic defect, establishing model with outer ring fault. Based on measured...
Abstract Bearing fault diagnosis is a critical component of the mechanical equipment monitoring system. In complex and harsh environment in which bearings operate, approach multi-source information fusion can extract features more stably extensively than traditional single-source method. However, most existing methods are infancy, there number pressing issues to address, such as subjective elements having significant impact, excessive data redundancy, fuzzy signal strategy, insufficient...
Abstract Strong noise in practical engineering environments interferes with the signal of a rolling bearing, which leads to decline diagnosis accuracy intelligent models. This paper proposes novel hybrid model (a convolutional denoising auto-encoder (CDAE)-BLCNN) address this problem. First, bearing vibration containing was input into CDAE, denoises through unsupervised learning and then outputs reconstructed data. Secondly, neural network (BLCNN), composed multi-scale wide convolution...
Abstract The robot joint is an important component of the construction robot, and its fault diagnosis can ensure exact execution building jobs, stable operation, timely prevention probable safety mishaps. However, deep learning‐based needs a multitude measured data, which difficult to obtain for various reasons. To solve problem insufficient digital twin‐assisted system joints proposed. First, simplified dynamics model developed generate virtual entity data be used as X‐domain twin model....
Abstract In order to address the limitations of condition monitoring multi-part coaxial structure equipment, which are due placement sensors and inability accurately identify weak faults, a fault diagnosis method based on convolutional neural network (CNN) transformer is proposed. Firstly, Transformer encoder constructed. The original signal then processed by preprocessing method, after fused form input. Finally, rich complementary features effectively extracted re-splicing data shape....
Bearing is an important component of rotating machinery. Intelligent bearings can realize self-diagnosis, self-regulation and self-adaptation for the bearings. The condition monitoring existing intelligent mostly monitors one parameter, which not enough bearing fault diagnosis. This paper proposes with a new structure, real-time various running state parameters. Firstly, proposed structure effectively increase number sensors improve signal-to-noise ratio. Secondly, considering strength be...
As an important part of the power source, motorized spindle is considered as components in high-end computer numerical control (CNC) machine tools, which required special attention to avoid expensive production shutdown due appearance massive failures. Therefore, it necessary detect incipient fault by establishing appropriate model. This article presents a scheme for detecting stator-winding shorted-turn motor system spindle. cornerstone model-based detection scheme, electromagnetic coupling...