Laijun Sun
Papers
2
Total Citations
5
H-Index
2
About
Laijun Sun is a researcher focused on advancing the reliability and safety of industrial automation systems, with a particular emphasis on fault diagnosis for Selective Compliance Assembly Robot Arms (SCARA). His work addresses the critical challenge of detecting and diagnosing mechanical faults in complex robotic systems, which are essential for maintaining safe and efficient production lines. Sun’s major contributions include developing innovative diagnostic methods that combine signal processing techniques with machine learning. His most cited paper, "SCARA mechanical fault identification based on WPM-SE+BPNN method" (2022, 3 citations), introduces a hybrid approach using wavelet packet multi-scale sample entropy and a backpropagation neural network for precise fault identification. Another notable work, "Research on SCARA Robot Fault Diagnosis Based on Hilbert-Huang Transform and Decision Tree" (2021, 2 citations), proposes a feature extraction method using Hilbert-Huang transform to improve detection efficiency. Though early in his career, Sun’s research is laying important groundwork for intelligent fault diagnosis in robotics, with potential to enhance predictive maintenance and operational safety in manufacturing. His work represents a valuable step toward more autonomous and reliable industrial systems.
Research Focus
Key Achievements
Top Papers
- 1SCARA mechanical fault identification based on WPM-SE+BPNN method3 citations · 2022
- 2