Nidal Alsayyed is a PhD researcher at the University of Central Florida testing real-time digital twins for predictive maintenance, with an emphasis on secure/robust telemetry and decision-making for industrial systems. In addition, Nidal work as a Security Operations Center (SOC) Analyst supporting enterprise incident detection and response. This role informs my research perspective, especially around data integrity, trustworthy telemetry, and cyber-aware decision-making in connected operational environments.
Research focus: Digital Twins for cyber-aware predictive maintenance (Smart-Twin-PM)
This research develops Smart Twin PM, a six-layer digital twin system for predictive maintenance in manufacturing. By combining real-time data analytics, physics-based validation, cybersecurity checks, and smart scheduling, it reduces unexpected failures by 15% and false alarms by 20%, enabling proactive, trustworthy, and efficient machine maintenance.
