Papers
7
Total Citations
78
H-Index
5
About
Corbinian Nentwich is a leading researcher in predictive maintenance and condition monitoring for industrial robots, a field critical to maximizing productivity and minimizing downtime in modern manufacturing. His work centers on developing data-driven models and robust data acquisition strategies to monitor the health of industrial robot gears. Nentwich’s key contributions include proposing a novel health indicator based on vibration data, validated through a rigorous evaluation method, and creating a combined anomaly and trend detection system for automated gear monitoring. His research, which has garnered over 78 citations, demonstrates both technical depth and practical impact—most notably through a cost-benefit analysis that provides a framework for justifying condition monitoring investments. By comparing data sources and modeling approaches, Nentwich has helped bridge the gap between theoretical prognostics and real-world industrial application, making his work essential reading for engineers and researchers aiming to implement reliable, cost-effective predictive maintenance in automated production lines.
Research Focus
Key Achievements
Top Papers
- 1Towards Data Acquisition for Predictive Maintenance of Industrial Robots21 citations · 2021
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- 5Cost-Benefit Analysis of Industrial Robot Gear Condition Monitoring7 citations · 2022
- 6Comparison of Data Sources for Robot Gear Condition Monitoring5 citations · 2022
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