Kumoh National Institute of Technology, South Korea.
Okwuosa Chibuzo Nwabufo is a Research Ph.D. Scholar at Kumoh National Institute of Technology 🇰🇷, South Korea, specializing in Mechanical Engineering. With a strong foundation in machine learning, deep learning, and real-time fault diagnostics, his work emphasizes bridging theoretical innovation with industrial application. Chibuzo is passionate about Prognostics and Health Management (PHM), Explainable AI (XAI), and digital twin technologies, aiming to create smart, AI-driven maintenance systems for next-generation industries.
Profile
🎓 Education
Chibuzo earned both his Master’s and is currently pursuing his Ph.D. in Mechanical Engineering from Kumoh National Institute of Technology, South Korea. His academic focus has been consistently rooted in intelligent fault diagnostics, predictive maintenance, and real-time monitoring technologies.
💼 Experience
With over four completed and two ongoing research projects, Chibuzo has hands-on experience in both academia and industry. Notable projects include real-time diagnostics for diaphragm pumps, fault analysis in induction motors, and zinc phosphating coating processes. He has collaborated on industry-sponsored projects and led initiatives involving advanced data-driven solutions for predictive maintenance.
🔬 Research Interests
His key research domains include:
🔧 Prognostics and Health Management (PHM)
🤖 Machine Learning & Deep Learning
🧠 Explainable AI (XAI)
🌐 Digital Twin Technologies
⚙️ Real-time Fault Diagnostics
🏆 Awards & Grants
Chibuzo’s research has been supported by prestigious Korean government grants:
IITP Innovative Human Resource Development for Local Intellectualization
ITRC Program (MSIT, Korea)
These grants facilitated collaborations with industry leaders and funded cutting-edge research in diagnostics and manufacturing innovation.
📚 Selected Publications
🆕 Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems
📅 2025-04-13 | Sensors
🔗 DOI: 10.3390/s25082449
👨🔬 Contributors: Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora, Jang-Wook Hur
📌 Application of deep learning and high-resolution imaging for defect detection on challenging industrial surfaces.
⚙️ A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance
📅 2024-10-09 | Journal of Sensor and Actuator Networks
🔗 DOI: 10.3390/jsan13050064
👨🔬 Contributors: Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa, Baek Cheon Shin, Jang-Wook Hur
📌 Fault detection in mechanical components using spectral features and XGBoost.
🔍 Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection
📅 2024-02-29 | Electronics
🔗 DOI: 10.3390/electronics13050926
👨🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Intelligent transformer fault diagnosis using statistical signal analysis and feature engineering.
⚡ Enhancing Transformer Core Fault Diagnosis and Classification through Hilbert Transform Analysis of Electric Current Signals
📅 2024-01-18 | Preprint
🔗 DOI: 10.20944/preprints202401.1371.v1
👨🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Preprint focusing on enhanced signal processing for electrical fault classification.
🧠 An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning
📅 2023 | IEEE Access
🔗 DOI: 10.1109/ACCESS.2023.3266865
👨🔬 Contributors: Chibuzo Nwabufo Okwuosa, Jang-Wook Hur
📌 Machine learning-based fault classification in squirrel cage induction motors under low-load conditions.