Houssem Habbouche
Papers
1
Total Citations
2
H-Index
1
About
Houssem Habbouche is a researcher advancing the reliability of critical mechanical systems, with a primary focus on fault diagnosis, condition monitoring, and intelligent maintenance for industrial machinery. His most-cited work introduces a robust methodology for planetary gearbox fault diagnosis by integrating time–frequency analysis with transfer learning, addressing a key challenge in power transmission systems used across energy generation, transportation, and robotics. This contribution is particularly significant as planetary gearboxes are essential yet prone to failure in demanding environments. With 2 citations already for this recent 2026 paper, Habbouche’s research is gaining traction for its practical, data-driven approach to detecting faults without requiring extensive labeled training data. His work sits at the intersection of mechanical engineering and artificial intelligence, aiming to improve operational safety and reduce downtime in critical infrastructure. Habbouche’s contributions are especially relevant for students and researchers interested in smart manufacturing, predictive maintenance, and the application of deep learning to real-world mechanical systems.
Research Focus
Key Achievements
Top Papers
- 1