Toyosi Ademujimi
Papers
2
Total Citations
53
H-Index
2
About
Toyosi Ademujimi is a leading researcher in smart manufacturing and industrial resilience, with a focus on fault diagnostics and digital twin technologies. Her work bridges the gap between engineering models and data-driven machine learning, particularly through the innovative use of Bayesian Networks (BNs) for real-time fault detection and system performance assurance. Her most-cited paper, "Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems" (2022, 45 citations), introduces a co-simulation approach that leverages digital twins to generate training data for BNs, enabling rapid diagnosis of faults in complex manufacturing environments. Building on this, her 2024 paper proposes a model-driven BN learning framework for factory-level diagnostics, using key performance indicators like Overall Equipment Effectiveness (OEE) to enhance system resilience and sustainability. Ademujimi’s contributions are particularly notable for integrating engineering models with probabilistic reasoning, offering scalable solutions for industry 4.0. Her work has been recognized for its potential to transform fault diagnostics from reactive to predictive, reducing downtime and improving product quality. With a growing citation record and a clear trajectory toward resilient, data-driven manufacturing, Ademujimi is a rising voice in the field of industrial informatics and smart systems engineering.
Research Focus
Key Achievements
Top Papers
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