Amirabbas Bahador

Agency for Science, Technology and Research

Papers

Total Citations

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About

Amirabbas Bahador is a researcher focused on advancing industrial automation and intelligent manufacturing, with key contributions in condition monitoring and predictive maintenance. His work centers on integrating real-time data analytics with cyber-physical systems to enhance machine reliability and process efficiency. His most-cited paper, "Condition Monitoring for Predictive Maintenance of Machines and Processes in ARTC Model Factory" (2021), has garnered 5 citations and demonstrates a practical framework for deploying sensor-driven diagnostics in smart factory environments. This research is notable for its application within the Advanced Remanufacturing and Technology Centre (ARTC) model factory, bridging the gap between theoretical maintenance strategies and industrial implementation. Bahador’s contributions are particularly relevant to Industry 4.0 initiatives, where reducing downtime and optimizing asset lifecycles are critical. By combining machine learning techniques with real-world factory data, his work provides actionable insights for engineers and researchers seeking to transition from reactive to predictive maintenance paradigms. His findings underscore the importance of scalable, data-driven approaches in modern manufacturing, making his research a valuable resource for those exploring digital twin technologies and smart maintenance systems.