Maik Horn

Papers

Total Citations

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About

Maik Horn is a researcher advancing the reliability and safety of robotic systems through the condition monitoring of strain wave gears (SWGs)—the critical speed reducers found in most robot joints. His work focuses on fault detection and simulation for sensorized SWGs, where strain gauges mounted on the flexible spline provide torque signals without external measurement equipment. Horn’s key contributions include developing a comprehensive simulation chain that models the behavior of these sensorized gears, enabling the detection of wear, degradation, and other faults that pose risks to robotic operation. His most-cited papers, including "Fault Detection in Gauge-Sensorized Strain Wave Gears" (2024, 3 citations) and "Simulation chain for sensorized strain wave gears" (2023, 3 citations), establish a foundation for model-based fault diagnosis, with his latest work in 2025 extending these capabilities. Though early in his career, Horn’s research directly addresses the growing need for safe, autonomous robots by providing tools to predict and prevent gear failures—a vital step toward more reliable industrial and service robotics.