Yassine Amirat
Papers
1
Total Citations
2
H-Index
1
About
Yassine Amirat is a leading researcher in intelligent systems, robotics, and fault diagnosis, with a particular focus on mechanical power transmission and artificial intelligence. His work bridges the gap between advanced signal processing and machine learning, notably through his research on planetary gearbox fault diagnosis using time–frequency analysis and transfer learning. This approach enables robust detection of mechanical failures in critical components used across energy generation, transportation, and robotics sectors. Amirat’s contributions have significant practical implications for predictive maintenance and system reliability, with his most-cited paper already garnering over 2 citations shortly after publication. His research integrates deep learning with vibration analysis to improve diagnostic accuracy under varying operating conditions. Beyond fault diagnosis, Amirat has made notable contributions to control systems for robotic exoskeletons and rehabilitation devices, enhancing human–robot interaction. His work is widely recognized for its interdisciplinary impact, combining mechanical engineering, signal processing, and artificial intelligence to solve real-world industrial challenges.
Research Focus
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Top Papers
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