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.

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