Riccardo Pinto
Papers
1
Total Citations
47
H-Index
1
About
Riccardo Pinto is a leading researcher in industrial robotics and predictive maintenance, with a focus on data-driven fault detection and remaining useful life estimation for robotic systems. His most-cited work, “Robot fault detection and remaining life estimation for predictive maintenance” (2019, 47 citations), outlines a comprehensive methodology for implementing predictive maintenance in robotics, spanning from data collection and dataset design to the application of advanced machine learning algorithms. This contribution has been instrumental in shifting maintenance strategies from reactive to proactive, enhancing operational efficiency and reducing downtime in manufacturing environments. Pinto’s research integrates sensor data analysis, anomaly detection, and degradation modeling, providing practical frameworks for real-time health monitoring of robotic assets. His work is widely recognized for bridging the gap between theoretical algorithm development and industrial application, making him a key figure in the advancement of smart manufacturing and Industry 4.0. Through his publications, Pinto continues to influence both academic research and practical implementations in robotics reliability and maintenance engineering.
Research Focus
Key Achievements
Top Papers
- 1