Jozsef Nagy | Engineering | Innovative Research Award

Posted on by Phenomenological Research

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]

References

  1. Elsevier. (n.d.). Scopus author details: Jozsef Nagy, Author ID 57906971900. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57906971900
  2. 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
  3. 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
  4. 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
  5. 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

Ing. Hem Bahadur Motra | Engineering | Best Researcher Award

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Dr. Ing. Hem Bahadur Motra | Engineering | Best Researcher Award 

Lecturer at University of Kiel | Germany

Dr.Ing. Hem Bahadur Motra is a highly accomplished researcher and academic specializing in geomechanics, rock physics, and geotechnical engineering at the University of Kiel, Germany. His professional journey reflects a deep commitment to advancing the understanding of subsurface processes through innovative experimental and computational approaches. He holds advanced degrees in civil and structural engineering and has completed extensive postdoctoral research in geotechnics, rock mechanics, and subsurface physics. As a research associate and head of the Geomechanics and Rock Mechanics Experimental Laboratory at Kiel University, he has contributed significantly to the study of the mechanical, thermal, and acoustic behavior of geomaterials under complex in-situ conditions. His multidisciplinary expertise bridges geosciences, civil engineering, and energy technologies, integrating rock physics, structural mechanics, and environmental sustainability. Dr.Ing. Hem Bahadur Motra has authored 47 scientific documents with 625 citations across 537 publications and holds an h-index of 14, reflecting the strong academic impact of his research. His extensive publication record spans top-tier journals, including International Journal of Rock Mechanics and Mining Sciences, Applied Energy, and Geotechnical and Geological Engineering. His research interests encompass multiscale geomechanical modeling, seismic anisotropy, CO₂ storage, geothermal energy, and the use of artificial intelligence in geological systems. He has led and collaborated on numerous national and international research projects funded by organizations such as DFG, DAAD, and the EU, focusing on sustainable infrastructure, rock deformation, and energy geotechnics. Beyond research, Dr.Ing. Hem Bahadur Motra plays an influential role as an editorial board member and special issue editor for several prestigious journals, contributing to the dissemination of cutting-edge developments in the field. His leadership in scientific committees, professional societies, and technical working groups further highlights his commitment to advancing interdisciplinary collaboration. Through his pioneering research, academic mentorship, and global collaborations, Dr.Ing. Hem Bahadur Motra continues to make impactful contributions to the scientific community, shaping the future of geotechnical and rock mechanics research and promoting innovative, sustainable solutions to address the challenges of modern engineering and Earth sciences.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

  • Motra, H. B.(2018). Geomechanical rock properties using pressure and temperature dependence of elastic P- and S-wave velocities. Geotechnical and Geological Engineering, 36(6), 3751–3766.

  • Motra, H. B.(2018). Poisson’s ratio and auxetic properties of natural rocks. Journal of Geophysical Research: Solid Earth, 123(2), 1161–1185.

  • Motra, H. B.(2019). Laboratory evaluation of rock-based geopolymers for zonal isolation and permanent P&A applications. Journal of Petroleum Science and Engineering, 175, 352–362.

  • Motra, H. B.(2017). Meso-scale modeling of heat transport in a heterogeneous cemented geomaterial by lattice element method. Granular Matter, 19, 1–12.

  • Motra, H. B.(2021). Pressure, temperature and lithological dependence of seismic and magnetic susceptibility anisotropy in amphibolites and gneisses from the central Scandinavian Caledonides. Tectonophysics, 820, 229113.

Wenkun Yang | Engineering | Best Researcher Award

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Dr. Wenkun Yang | Engineering | Best Researcher Award

Research associate at Hohai University, China.

Dr. Wenkun Yang is an accomplished researcher in the field of rock mechanics, tunneling, and TBM (Tunnel Boring Machine) technology. His contributions to the field focus on integrating advanced machine learning techniques for rock stability analysis and predictive modeling in underground construction. With 11 Scopus-indexed publications and over 261 citations, Dr. Yang has made a significant impact on geotechnical engineering research. He has authored two books and filed four patents, further demonstrating his innovation in the domain. His work has been recognized in top-tier journals such as Tunnelling and Underground Space Technology and Rock Mechanics and Rock Engineering. Beyond academia, Dr. Yang has collaborated with leading institutions and industry partners, contributing to several high-profile engineering projects. His expertise in numerical modeling, data-driven decision-making, and smart TBM operations has led to groundbreaking advancements in underground infrastructure development. With a strong track record of scientific publications, industrial collaborations, and editorial contributions, he stands as a prominent figure in his field. His ability to bridge theoretical research with practical applications makes him a strong candidate for the Best Researcher Award. His dedication to advancing tunneling technology and his impact on engineering practices continue to earn him recognition in both academic and industrial circles.

Professional Profile:

Education

Dr. Wenkun Yang holds a Ph.D. in Geotechnical Engineering, where his doctoral research focused on integrating artificial intelligence and numerical modeling for rock mechanics applications. His academic journey began with a Bachelor’s degree in Civil Engineering, followed by a Master’s degree specializing in underground engineering. Throughout his educational career, he developed a strong foundation in computational geomechanics, material behavior analysis, and advanced simulation techniques. His research during his Master’s studies emphasized the stability assessment of rock masses in deep tunnels, setting the stage for his later work in TBM technology. During his Ph.D., he worked extensively on data-driven approaches to rock engineering, combining traditional empirical models with machine learning algorithms to enhance prediction accuracy in geological conditions. His education has been complemented by advanced certifications in artificial intelligence applications in engineering and high-performance computing. His academic excellence has been recognized through scholarships and research grants, allowing him to study in collaborative environments with international experts in tunneling and rock engineering. His multi-disciplinary education spanning structural engineering, computational modeling, and artificial intelligence has equipped him with the necessary skills to address complex geotechnical challenges. Dr. Yang’s rigorous academic background forms the foundation for his innovative contributions to the field of underground construction and rock mechanics.

Professional Experience

Dr. Wenkun Yang has extensive professional experience in both academic and industrial settings, making significant contributions to underground engineering and rock mechanics. He currently serves as a senior researcher at a leading geotechnical institute, where he oversees multiple projects on TBM technology and tunneling stability. His role involves leading research teams, mentoring junior researchers, and developing computational models for geotechnical risk assessments. Prior to this position, he worked as a postdoctoral researcher at a renowned university, where he contributed to high-impact projects focusing on intelligent TBM monitoring systems. His industry experience includes collaborations with major engineering firms and governmental agencies, where he applied his research to real-world tunnel construction projects. He has played a crucial role in consulting for large-scale infrastructure developments, providing expertise on ground deformation prediction and machine learning-based tunneling strategies. In addition to his research roles, Dr. Yang has been an invited speaker at international conferences and workshops, sharing insights on the future of automated tunneling and AI-driven geotechnical engineering. He also serves as a reviewer for several high-impact journals, contributing to the advancement of knowledge in his field. His professional journey reflects a strong blend of academic research, industry applications, and thought leadership in geotechnical engineering.

Research Interests

Dr. Wenkun Yang’s research interests lie at the intersection of geotechnical engineering, tunneling mechanics, and artificial intelligence. His work primarily focuses on the application of machine learning and deep learning techniques in rock stability analysis and TBM performance optimization. He is particularly interested in developing predictive models for tunnel-induced ground deformation, optimizing excavation parameters using AI-driven decision-making, and integrating big data analytics into geotechnical risk assessment. Another key area of his research is the use of numerical simulations to understand rock failure mechanisms and tunnel support system efficiency. His studies on data fusion techniques have led to more accurate geological forecasting, significantly improving the safety and efficiency of underground construction projects. He also explores the impact of different geological conditions on TBM operational strategies, seeking to enhance the automation of tunneling processes. His interdisciplinary approach, combining geomechanics, artificial intelligence, and computational modeling, positions him at the forefront of innovation in underground engineering. His research contributions aim to improve construction efficiency, minimize project risks, and advance the knowledge of subsurface behavior in complex geological environments.

Research Skills

Dr. Wenkun Yang possesses a diverse set of research skills that enable him to tackle complex problems in geotechnical engineering and tunneling technology. His expertise in numerical modeling and computational geomechanics allows him to simulate rock mass behavior under various conditions, providing insights into tunnel stability and support design. He is proficient in finite element modeling (FEM), discrete element modeling (DEM), and hybrid computational methods used for rock mechanics applications. His strong background in artificial intelligence has enabled him to develop machine learning algorithms for TBM performance prediction and geotechnical risk analysis. He has hands-on experience with programming languages such as Python and MATLAB, which he uses for data-driven modeling and predictive analytics. Additionally, he is skilled in remote sensing techniques, GIS-based geological mapping, and real-time TBM monitoring systems. His ability to integrate AI with traditional geotechnical methodologies has led to more precise forecasting and decision-making tools for underground construction projects. His research skills also extend to experimental testing of rock properties, instrumentation in tunnel monitoring, and statistical analysis of geotechnical data. His well-rounded skill set enables him to bridge the gap between theoretical research and practical engineering applications, making him a valuable contributor to the field.

Awards and Honors

Dr. Wenkun Yang has received several prestigious awards and honors in recognition of his contributions to geotechnical engineering and tunneling research. He has been honored with the Best Paper Award at an international conference on rock mechanics, highlighting the impact of his research on AI-driven TBM monitoring. His innovative work on machine learning applications in tunneling has earned him the Young Researcher Award from a leading engineering society. Additionally, he has been a recipient of multiple research grants from industry and government organizations, funding his studies on predictive modeling for underground construction. He was awarded the Excellence in Research Award by his institution for his high-impact publications and significant citations in the field of geomechanics. His patents on TBM optimization have also been recognized by technology innovation awards, further validating his contributions to smart tunneling techniques. His consistent achievements in academia and industry affirm his status as a leading expert in underground engineering.

Conclusion

Dr. Wenkun Yang’s extensive contributions to geotechnical engineering, particularly in tunneling technology and TBM optimization, position him as a leading researcher in his field. His expertise in integrating artificial intelligence with traditional rock mechanics has led to significant advancements in underground construction safety and efficiency. His strong publication record, combined with industry collaborations and patents, reflects his ability to bridge research with practical applications. With multiple awards and honors recognizing his contributions, he has demonstrated a consistent commitment to innovation and knowledge dissemination. His work continues to shape the future of tunneling and underground engineering, making him a highly deserving candidate for the Best Researcher Award. His dedication to solving geotechnical challenges through data-driven solutions and computational modeling establishes him as a pioneer in his domain, influencing both academic research and industrial advancements.

Publication Top Notes

  • Feature fusion method for rock mass classification prediction and interpretable analysis based on TBM operating and cutter wear data
    📅 2025 | 📜 Tunnelling and Underground Space Technology
    ✍️ Authors: Yang, W.; Chen, Z.; Zhao, H.; Chen, S.; Shi, C.
    🔗 DOI: 10.1016/j.tust.2024.106351
    📑 EID: 2-s2.0-85213873575
  • Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods
    📅 2023 | 📜 Underground Space (China)
    ✍️ Authors: Li, J.-B.; Chen, Z.-Y.; Li, X.; Jing, L.-J.; Zhang, Y.-P.; Xiao, H.-H.; Wang, S.-J.; Yang, W.-K.; Wu, L.-J.; Li, P.-Y.
    🔗 DOI: 10.1016/j.undsp.2023.01.001
    📑 EID: 2-s2.0-85151779831
  • Feedback on a shared big dataset for intelligent TBM Part II: Application and forward look
    📅 2023 | 📜 Underground Space (China)
    ✍️ Authors: Li, J.-B.; Chen, Z.-Y.; Li, X.; Jing, L.-J.; Zhang, Y.-P.; Xiao, H.-H.; Wang, S.-J.; Yang, W.-K.; Wu, L.-J.; Li, P.-Y.
    🔗 DOI: 10.1016/j.undsp.2023.01.002
    📑 EID: 2-s2.0-85152230288
  • Probabilistic machine learning approach to predict incompetent rock masses in TBM construction
    📅 2023 | 📜 Acta Geotechnica
    ✍️ Authors: Yang, W.; Zhao, J.; Li, J.; Chen, Z.
    🔗 DOI: 10.1007/s11440-023-01871-y
    📑 EID: 2-s2.0-85151297550
  • Probabilistic model of disc-cutter wear in TBM construction: A case study of Chaoer to Xiliao water conveyance tunnel in China
    📅 2023 | 📜 Science China Technological Sciences
    ✍️ Authors: Yang, W.K.; Chen, Z.Y.; Wu, G.S.; Xing, H.
    🔗 DOI: 10.1007/s11431-023-2465-y
    📑 EID: 2-s2.0-85175035176
  • Excavation rate “predicting while tunnelling” for double shield TBMs in moderate strength poor to good quality rocks
    📅 2022 | 📜 International Journal of Rock Mechanics and Mining Sciences
    ✍️ Authors: Mu, B.; Yang, W.; Zheng, Y.; Li, J.
    🔗 DOI: 10.1016/j.ijrmms.2021.104988
    📑 EID: 2-s2.0-85120046745
  • Significance and methodology: Preprocessing the big data for machine learning on TBM performance
    📅 2022 | 📜 Underground Space (China)
    ✍️ Authors: Xiao, H.-H.; Yang, W.-K.; Hu, J.; Zhang, Y.-P.; Jing, L.-J.; Chen, Z.-Y.
    🔗 DOI: 10.1016/j.undsp.2021.12.003
    📑 EID: 2-s2.0-85124407862
  • Numerical simulation for compressive and tensile behaviors of rock with virtual microcracks
    📅 2021 | 📜 Arabian Journal of Geosciences
    ✍️ Authors: Chen, X.; Shi, C.; Ruan, H.-N.; Yang, W.-K.
    🔗 DOI: 10.1007/s12517-021-07163-7
    📑 EID: 2-s2.0-85105802718
  • Calibration of micro-scaled mechanical parameters of granite based on a bonded-particle model with 2D particle flow code
    📅 2019 | 📜 Granular Matter
    ✍️ Authors: Not provided
    🔗 DOI: 10.1007/s10035-019-0889-3
  • Numerical simulation of column charge explosive in rock masses with particle flow code
    📅 2019-11 | 📜 Granular Matter
    ✍️ Authors: Not provided
    🔗 DOI: 10.1007/s10035-019-0950-2
  • Study of Anti-Sliding Stability of a Dam Foundation Based on the Fracture Flow Method with 3D Discrete Element Code
    📅 2017-10-06 | 📜 Energies
    ✍️ Authors: Chong Shi; Wenkun Yang; Weijiang Chu; Junliang Shen; Yang Kong
    🔗 DOI: 10.3390/en10101544