Hongyi Bai
Papers
1
Total Citations
3
H-Index
1
About
Hongyi Bai is a researcher focused on intelligent fault diagnosis and condition monitoring in industrial robotics, with a particular emphasis on Selective Compliance Assembly Robot Arms (SCARA). His work addresses the critical challenge of ensuring operational safety in automated production lines by developing advanced signal processing and machine learning techniques for mechanical fault identification. In his most cited paper, "SCARA mechanical fault identification based on WPM-SE+BPNN method" (2022), Bai introduced a novel hybrid approach combining wavelet packet multi-scale entropy (WPM-SE) for feature extraction with a backpropagation neural network (BPNN) for classification. This method effectively handles the complexity of SCARA systems, which are prone to diverse mechanical faults and unstable movements. While his citation count is currently modest at 3, the work represents a foundational contribution to predictive maintenance in manufacturing, offering a practical framework for real-time fault detection. Bai’s research bridges theoretical signal analysis with industrial application, positioning him as an emerging voice in the field of robotic reliability and smart manufacturing.
Research Focus
Key Achievements
Top Papers
- 1SCARA mechanical fault identification based on WPM-SE+BPNN method3 citations · 2022