Urko Zurutuza
Papers
4
Total Citations
92
H-Index
3
About
Urko Zurutuza is a leading researcher in industrial robotics, specializing in predictive maintenance and health assessment for manufacturing systems. His work focuses on developing non-intrusive, data-driven methodologies to monitor and optimize the performance of industrial robots, ensuring reliability and efficiency in automated production lines. Zurutuza's major contributions include the design of a synchronized data acquisition network architecture for predictive maintenance, as detailed in his 2021 paper, which has garnered 47 citations for its practical impact on manufacturing assembly lines. He also pioneered a vision-based approach to assess accuracy degradation in robotics (29 citations) and introduced torque signature analysis for joint health assessment, a method that uses torque sensor data to create digital signatures for early fault detection. His research on standby pose optimization further enhances robot longevity by minimizing wear during idle periods. With a growing citation record, Zurutuza's work bridges advanced sensor technology and real-world industrial applications, offering scalable solutions for smart factories. His achievements underscore a commitment to extending robot lifespan and reducing downtime, making him a key figure in the evolution of resilient, self-aware manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4