Dr. Pascal Vrignat | Industry 4.0 | Research Excellence Award
Prisme Laboratory at Orleans University | France
Pascal Vrignat is a researcher specializing in operational safety, diagnostics, prognostics, and maintenance strategies for complex systems, with particular expertise in Markovian and stochastic models. His work significantly advances methods for estimating system degradation using survival laws, hidden Markov models, and Remaining Useful Life approaches. He contributes to understanding system obsolescence and managing shortages across the life cycle of industrial systems. His research bridges theory and industrial application, encompassing industrial computing, advanced process control, human–machine interfaces, SCADA systems, IoT, M2M technologies, and digital communication protocols, including OPC-based architectures. He has an extensive record of scientific output, including journal publications, conference papers, book chapters, and a widely used textbook on industrial local networks. His recent works address bearing degradation monitoring and the role of AI in sustainability-focused applications. He is active in research project development, editorial responsibilities, and academic leadership within his institution and research laboratory. His contributions to industry-oriented R&D have earned recognition in international automation competitions. His scholarly impact is reflected in 618 citations (405 since 2020), an h-index of 10 (7 since 2020), and an i10-index of 13 (6 since 2020), underscoring his sustained influence in the fields of reliability engineering, automation, predictive maintenance, and digital industrial systems.
Profiles: Orcid | Google Scholar
Featured Publications
Vrignat, P., Kratz, F., & Avila, M. (2022). Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, 218, 108140. https://doi.org/10.1016/j.ress.2021.108140
Cited by: 152
Pascal, V., Toufik, A., Manuel, A., Florent, D., & Kratz, F. (2019). Improvement indicators for total productive maintenance policy. Control Engineering Practice, 82, 86–96. https://doi.org/10.1016/j.conengprac.2018.09.019
Cited by: 81
Vrignat, P., Avila, M., Duculty, F., & Kratz, F. (2015). Failure event prediction using hidden Markov model approaches. IEEE Transactions on Reliability, 64(3), 1038–1048. https://doi.org/10.1109/TR.2015.2426458
Cited by: 49
Aggab, T., Avila, M., Vrignat, P., & Kratz, F. (2021). Unifying model-based prognosis with learning-based time-series prediction methods: Application to Li-ion battery. IEEE Systems Journal, 15(4), 5245–5254. https://doi.org/10.1109/JSYST.2021.3080125
Cited by: 32
Vrignat, P., Avila, M., Duculty, F., Aupetit, S., Slimane, M., & Kratz, F. (2012). Maintenance policy: Degradation laws versus Hidden Markov Model availability indicator. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 226(2), 137–155. https://doi.org/10.1177/1748006X11406335
Cited by: 21