Nagi Gebraeel
Papers
2
Total Citations
11
H-Index
2
About
Nagi Gebraeel is a leading researcher in industrial engineering, whose work centers on data-driven decision-making for complex, high-stakes systems. His primary research areas include predictive maintenance, spare parts logistics, and fault diagnosis, with a particular focus on applications in space habitats and advanced manufacturing. A major contribution is his development of a stochastic programming framework that jointly optimizes maintenance schedules and spare parts provisioning for deep space habitats, a critical innovation for ensuring mission sustainability in resource-constrained environments. This work, published in 2023, has already garnered 8 citations, reflecting its timely importance. More recently, Gebraeel has pioneered the Deep Complex Wavelet Denoising Network, a novel approach for interpretable fault diagnosis of industrial robots. This method addresses the dual challenges of strong noise interference and imbalanced data, enabling more reliable detection of subtle fault signatures while preserving physical interpretability—a key limitation of many black-box models. His research bridges the gap between theoretical optimization and practical deployment, making him a notable figure in resilient and autonomous industrial systems.
Research Focus
Key Achievements
Top Papers
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