Imanol Andonegui
Papers
4
Total Citations
92
H-Index
3
About
Imanol Andonegui is a leading researcher in industrial robotics, specializing in predictive maintenance, health assessment, and performance optimization for manufacturing systems. His work centers on developing non-intrusive, data-driven methodologies to monitor and extend the lifespan of industrial robots, particularly in assembly lines. Andonegui’s most impactful contribution is a practical, synchronized data acquisition network architecture for predictive maintenance, published in 2021 and cited 47 times, which enables real-time fault detection and reduces downtime. He also pioneered a vision-based approach to assess accuracy degradation in robotics, earning 29 citations, and introduced torque signature analysis for joint health assessment, a method that uses torque sensor data to create digital signatures for non-intrusive diagnostics. His torque-based methodology for optimizing robot standby poses further enhances energy efficiency and component longevity. With over 90 total citations, Andonegui’s work bridges theoretical advances and experimental implementation, offering scalable solutions for Industry 4.0. His research is vital for engineers seeking to improve reliability and reduce costs in automated manufacturing, making him a key figure in the evolution of smart, self-maintaining robotic systems.
Research Focus
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Top Papers
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