Anshul Kumar
Papers
1
Total Citations
6
H-Index
1
About
Anshul Kumar is a researcher whose work sits at the intersection of robotics, automation, and intelligent fault diagnosis. His primary research focus is on developing data-driven methodologies for the detection of intermittent faults in complex electromechanical systems—a critical challenge for ensuring operational safety and reliability. Kumar’s most notable contribution, "Intermittent fault detection for MIMO systems: a case study on SCARA robot" (2023), presents a rigorous comparative study of data-driven approaches for estimating actuator torques under varying conditions. This work, which has already garnered 6 citations, demonstrates how advanced signal processing and machine learning can be leveraged to preemptively identify subtle, non-permanent failures in multi-input multi-output (MIMO) robotic platforms. By applying these techniques to a real-world SCARA robot, Kumar bridges the gap between theoretical fault detection algorithms and practical industrial automation needs. His research is particularly valuable for engineers and students working on predictive maintenance, robotics control, and system health monitoring, offering a clear pathway toward safer, more autonomous manufacturing environments.
Research Focus
Key Achievements
Top Papers
- 1