Alaa Abdulhady Jaber
University of Technology - Iraq, Newcastle University, University of Technology
Papers
20
Total Citations
385
H-Index
12
About
Alaa Abdulhady Jaber is a prominent researcher specializing in condition monitoring, fault diagnosis, and intelligent embedded systems for industrial robots and autonomous vehicles. His work sits at the intersection of signal processing, artificial intelligence, and mechanical systems reliability, with a particular focus on applying discrete wavelet transform and artificial neural networks to detect and classify mechanical faults in real-world engineering systems. Jaber's most influential contributions center on developing robust fault detection frameworks for industrial robot components, including gears, bearings, and backlash mechanisms, with his 2016 paper on gear fault diagnosis accumulating 54 citations. His early and prolific output in 2016 established foundational methodologies combining statistical control charts, wireless sensor nodes, and embedded systems for practical condition monitoring — work that collectively reflects a systems-level approach to predictive maintenance. His more recent research extends these techniques to UAV unbalance fault classification, demonstrating his adaptability to emerging autonomous technologies. With over 300 cumulative citations across his most notable works, Jaber has made a measurable impact on the field of intelligent maintenance engineering. His research is particularly valuable for engineers and students seeking to implement AI-driven, cost-effective monitoring solutions in automated manufacturing and autonomous aerial systems.
Research Focus
Key Achievements
Top Papers
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- 2Industrial Robot Fault Detection Based on Statistical Control Chart44 citations · 2016
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