UE Diagnosis, Reliability & Maintenance

This course offers a comprehensive and practice-oriented introduction to safety, supervision, and fault diagnosis in modern industrial systems. Students explore the key principles of detecting and isolating faults in complex processes through three complementary approaches: model-based methods, signal-processing techniques, and data-driven analytics. The class combines solid theoretical foundations with hands-on laboratory sessions, where learners apply advanced supervision tools to real-world scenarios ranging from fluid systems and EEG-based sleep monitoring to mineral processing and environmental sensor networks. Designed for future engineers and researchers, this course provides the essential skills needed to ensure reliability, performance, and safety in today’s automated industrial plants.

Systems reliability and maintenance : the objective of this course is to provide a comprehensive foundation in system reliability theory and dependability analysis methods :

•    Probabilistic failure models and lifetime modelling of engineering components

•    Qualitative approaches to system reliability analysis (FMECA, ...)

•    Reliability modelling and analysis of systems and networks of independent components (Fault Tree Analysis, Reliability Block Diagrams, Event Tree Analysis, Structure Function, Minimal Cutsets, Importance Measures)

•    Markov processes for systems and networks reliability modelling (systems with dependent components, passive redundancies, tested components, ...)

•    Reliability of maintained systems and maintenance policies modelling

Reference textbooks :

•    S. Gentil (Ed.), "Supervision des procédés complexes", HERMES Systèmes automatisés, 2007.

•    Isermann, "Fault diagnosis systems", Springer, 2006.

•    Blanke, Kinnaert, Lunze, Staroswiecki, "Diagnosis and fault tolerant control", Springer, 2003.

•    Marvin Rausand, Arnljot Hoyland, System Reliability Theory - Models, Statistical Methods and Applications. Wiley Series in Probability and Statistics. 2004.

•    J.D. Andrews, T.R. Moss, Reliability and Risk Assessment. Longman Scientific & Technical. 1993

•    M. Modarres, What every engineer should know about Reliability and Risk Analysis. Marcel Dekker. 1993