Innovative Research Award
Jozsef Nagy
Széchenyi István University of Győr, Hungary
| Jozsef Nagy |
| Affiliation |
Széchenyi István University of Győr |
| Country |
Hungary |
| Scopus ID |
57906971900 |
| Documents |
6 |
| Citations |
15 |
| h-index |
2 |
| Subject Area |
Engineering |
| Event |
International Phenomenological Research Awards |
| ORCID |
0009-0001-1830-2707 |
The Innovative Research Award recognition highlights the scholarly and applied engineering contributions of Jozsef Nagy, an automotive engineering professional, executive leader, and researcher whose work bridges industrial quality systems, predictive maintenance, vehicle diagnostics, machine learning applications, and regulatory aspects of automotive data management. His professional and academic activities integrate more than two decades of experience within Audi AG and the Volkswagen Group with contemporary research addressing data-driven vehicle lifecycle management and advanced diagnostic methodologies.[1]
Abstract
Jozsef Nagy has developed a research portfolio focused on predictive maintenance, predictive repair, vehicle diagnostics, neural-network-supported vehicle simulation, industrial quality science, and automotive data governance. His work investigates methods for improving operational reliability and lifecycle management of modern vehicles through continuous monitoring, anomaly detection, data analytics, and regulatory compliance frameworks. The combination of industrial leadership and applied engineering research has resulted in publications addressing practical challenges within the automotive sector, particularly in predictive service methodologies and digital vehicle ecosystems.[2][3]
Keywords
Vehicle diagnostics; predictive maintenance; predictive repair; automotive engineering; neural networks; vehicle simulation; quality assurance; industrial optimization; automotive data management; cybersecurity; EU regulatory compliance; condition monitoring.
Introduction
The automotive industry is increasingly dependent on intelligent monitoring systems, connected vehicle technologies, and advanced analytics. Within this context, Jozsef Nagy’s research explores the convergence of engineering diagnostics, quality management, machine learning, and data governance. His investigations seek to improve predictive decision-making processes by leveraging operational data collected from vehicle systems, thereby supporting maintenance planning, failure prevention, and lifecycle optimization.[3][5]
Research Profile
Jozsef Nagy’s academic interests are closely aligned with practical engineering challenges encountered in large-scale vehicle manufacturing and quality management environments. His research emphasizes predictive maintenance methodologies, digital diagnostics, neural-network-based simulations, and automotive data utilization under evolving European regulatory frameworks. These activities are informed by extensive executive experience within Audi Hungaria, Audi AG, and the Volkswagen Group, where he has led engineering quality functions, vehicle launch programs, and quality assurance initiatives across multiple countries.[1]
- Predictive maintenance and predictive repair systems.
- Online vehicle diagnostics and anomaly detection.
- Neural networks for vehicle simulation and parameter estimation.
- Automotive data architecture and cybersecurity.
- Industrial quality science and production optimization.
Research Contributions
A significant contribution of Jozsef Nagy’s research concerns predictive repair methodologies for vehicle systems. His studies examine how continuous monitoring and micro-leakage detection can improve reliability assessments and maintenance planning for modern automotive components. Such approaches support a transition from reactive maintenance toward condition-based and predictive service models.[2]
Another important area involves the legal and technical dimensions of automotive data collection. His work evaluates data storage practices, online vehicle data acquisition, and regulatory compliance requirements within the European automotive sector. These investigations address emerging questions related to cybersecurity, data ownership, and digital vehicle ecosystems.[3]
Jozsef Nagy has also explored acoustic fingerprinting applications in vehicle manufacturing. This research investigates how sound-based analytical methods may support quality assurance processes, manufacturing diagnostics, and future industrial monitoring solutions within production environments.[4]
Publications
Jozsef Nagy’s most significant publications advance predictive vehicle maintenance, automotive data governance, and intelligent diagnostics, including studies on micro-leakage-based predictive repair and EU automotive data frameworks, contributing practical solutions for modern vehicle lifecycle management.[2][3][4][5]
Research Impact
The research output of Jozsef Nagy demonstrates the practical application of engineering science to industrial challenges. His publications contribute to discussions surrounding predictive maintenance, connected vehicle technologies, manufacturing diagnostics, and data-driven quality systems. By combining industrial experience with scholarly inquiry, his work supports the advancement of reliable and efficient vehicle lifecycle management approaches.[2][5]
Award Suitability
The body of work produced by Jozsef Nagy aligns with the objectives of the International Phenomenological Research Awards by demonstrating interdisciplinary engagement between engineering practice, data analytics, industrial quality science, and applied research. His investigations address contemporary challenges in predictive maintenance, vehicle diagnostics, and automotive data governance while maintaining relevance to both academic and industrial communities. The integration of executive leadership experience with research activities further strengthens the practical significance of his scholarly contributions.[1][3]
Conclusion
Jozsef Nagy represents a professional profile that combines extensive automotive industry leadership with emerging research in predictive diagnostics, machine-learning-supported engineering analysis, and automotive data management. His publications contribute to ongoing developments in predictive repair, intelligent diagnostics, manufacturing analytics, and regulatory compliance, providing a foundation for future research and practical implementation within the automotive sector.[2][4]
External Links
References
- Elsevier. (n.d.). Scopus author details: Jozsef Nagy, Author ID 57906971900. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57906971900
- Nagy, J., & Lakatos, I. (2026). Predictive Repair of Vehicle R1234yf Refrigerant Systems Based on Monitoring of Micro-Leakages. Machines. DOI: https://doi.org/10.3390/machines14030268
- Nagy, J., Karácsony, G., Kelemen, R., & Lakatos, I. (2025). Legal Framework and Data Storage Background of Online Collected Data for Predictive Maintenance and Repair Purposes in the Automotive Sector in the European Union. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2025.3594772
- Nagy, J., & Lakatos, I. (2025). Acoustic Fingerprint in Vehicle Manufacturing as a Basis for Future Applications. Pollack Periodica. DOI: https://doi.org/10.1556/606.2025.01260
- Nagy, J., & Lakatos, I. (2024). Predictive Maintenance and Predictive Repair of Road Vehicles—Opportunities, Limitations and Practical Applications. Engineering Proceedings. DOI: https://doi.org/10.3390/engproc2024079027