Papers
4
Total Citations
54
H-Index
3
About
Samir Khan’s research sits at the critical intersection of artificial intelligence, prognostic health management, and autonomous maintenance—fields essential for ensuring the reliability and longevity of complex engineering systems. His work focuses on developing robust AI-driven frameworks that can predict failures and optimize maintenance schedules, moving beyond traditional approaches that often falter under real-world, dynamic conditions. A key contribution is his pioneering exploration of how machine learning models can be made resilient enough for safety-critical applications, a challenge he tackles in his most-cited paper, “Robustness of AI-based prognostic and systems health management” (32 citations). Khan has also advanced the concept of “Through-Life Engineering” with his work on autonomous maintenance (16 citations), proposing systems that self-diagnose and schedule repairs without human intervention. Notably, he is pushing the boundaries of health monitoring for non-stationary machinery, such as robotic arms, whose complex, variable movements defy conventional diagnostic methods. His forward-looking perspective is evident in his research on “MRO 4.0,” where he maps the digitalization challenges and emerging technologies poised to transform aviation maintenance. Through these contributions, Khan is shaping a future where engineered systems are not only smarter but also more self-sufficient and reliable.
Research Focus
Key Achievements
Top Papers
- 1Robustness of AI-based prognostic and systems health management32 citations · 2021
- 2Autonomous Maintenance for Through-Life Engineering16 citations · 2014
- 3Towards Online Health Monitoring of Robotic Arm3 citations · 2023
- 4