Journal of Artificial Intelligence and Digital Health

Chibuzo Nwabufo is a Nigerian researcher and PhD candidate in Mechanical Engineering currently based in South Korea. He received his B.Eng. degree in Agricultural Engineering from Imo State University, Owerri, Nigeria, and his M.Eng. degree from Kumoh National Institute of Technology, Gumi, South Korea. His research interests span advanced diagnostics, machine learning, signal processing, and smart manufacturing technologies. Passionate about integrating artificial intelligence with real-world engineering systems, he specializes in fault detection, electric motor health monitoring, signal processing, digital twin development, and prognostics and health management (PHM) for mechanical systems. Chibuzo has developed strong expertise in big data analytics, deep learning, and 3D modeling. His work includes designing lightweight autoencoder architectures, building real-time diagnostic pipelines, implementing advanced feature engineering strategies, and exploring explainable AI to enhance transparency, safety, and reliability in complex engineering systems. He has contributed to multiple industry-sponsored projects, including predictive maintenance solutions for industrial shot-blast machines and process optimization for zinc-phosphate surface coatings. He has also collaborated with research laboratories involved in semiconductor process development, fuel cell technology, and AI-assisted smart manufacturing. His practical, application-oriented approach drives him to develop solutions that are not only theoretically sound but also deployable in real-time industrial environments. Chibuzo continues to explore innovative integrations of machine learning, signal processing, electrochemical energy systems, and robotics, with the long-term goal of building intelligent diagnostic frameworks and digital twin systems that improve performance, reliability, and safety across industrial sectors.

• AI-based Fault Diagnosis
• Industrial Automation & Smart Manufacturing
• Sensor Fusion & Intelligent Monitoring
• Signal Processing & Predictive Maintenance
• Condition Monitoring of Induction Motors (SCIM)
• Deep Learning for Defect Detection
• Machine Learning for Industrial Systems
• Feature Engineering & Feature Selection

1. Researcher at FD&P Lab
2. Research Assistant at Defense Reliability Laboratory
3. Excellence Paper Award (2023 Autumn Academic Conference)
4. Active involvement in advanced imaging systems, XGBoost-based fault detection, and autoencoder-LSTM fusion methods
5. Reviewer and contributor in multiple engineering and applied-science publications

1. Optimization of flow rate for uniform zinc phosphate coating on steel cylinders (2025)
2. Optimizing defect detection on glossy and curved surfaces using deep learning (2025)
3. Transformer core fault diagnosis via current signal analysis with Pearson correlation (2024)
4. Spectral-based blade fault detection in shot blast machines with XGBoost (2024)
5. Extruder machine gear fault detection using autoencoder LSTM via sensor fusion (2023)
6. Intelligent hybrid feature selection for SCIM inter-turn fault classification (2023)
7. FEA-assisted decision-making framework for PEMFC gasket material selection (2022)
8. Filter-based feature-engineering-assisted SVC fault classification (2022)
9. Cost-efficient MCSA-based fault diagnostic framework for SCIM (2022)