Baolong Wang
Papers
2
Total Citations
5
H-Index
2
About
Baolong Wang is a researcher focused on industrial robotics and intelligent fault diagnosis, with a particular emphasis on the Selective Compliance Assembly Robot Arm (SCARA). His work addresses the critical challenge of maintaining operational safety and efficiency in automated production lines by developing advanced methods for mechanical fault identification. Wang’s major contributions include pioneering the use of the Hilbert-Huang Transform combined with decision tree algorithms to extract and classify fault features from complex robotic systems, as well as proposing the WPM-SE+BPNN method—a hybrid approach that integrates wavelet packet multi-scale entropy with backpropagation neural networks for robust fault detection. Although his most-cited papers have accumulated modest citation counts (3 and 2 citations respectively), they represent foundational steps in applying signal processing and machine learning to SCARA robot diagnostics. Wang’s research is notable for its practical orientation, directly targeting the low efficiency and difficulty of detecting industrial equipment failures. His work is particularly relevant for students and researchers in mechatronics and condition monitoring, offering a clear pathway from theoretical signal analysis to real-world industrial applications.
Research Focus
Key Achievements
Top Papers
- 1SCARA mechanical fault identification based on WPM-SE+BPNN method3 citations · 2022
- 2