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Industrial Engineering Department

Risk Resilience Reliability Research Group


Our team is developing research activities for safety and risk analysis of complex engineered systems. Our models are mainly based on stochastic processes and data driven approaches with a strong focus on optimization and uncertainties quantification for decision making in design and operation. We are strongly connected to several industry partners with the chair Risk and Resilience of Complex System. This chair is supported by EDF, SNCF, Orange and Paris Airport. This is an arena to define study cases, share knowledge data and experiences, develop methods, implement benchmark and prototypes of tools. This chair is taking over the previous chair on Systems sciences and Energy Challenges supported by EDF.

Our research activity is organized around 3 main studied objects :

• Complex systems and infrastructures, cyber-physi-cal systems

The analysis of these systems cannot be carried out only with classical methods of system decomposition and logic analysis. A framework is needed to integrate a number of methods capable of viewing the problem from different perspectives (topological and functional, static and dynamic, discrete and continuous...), properly treating uncertainties by probabilistic and non-probabilistic methods. Our main contribution is to use stochastic processes, data driven approaches and Monte Carlo simulation to identify infl uent parameters and critical items, and to defi ne proper level of abstractions for modelling. The modelling work is achieved in the perspective of providing decision indicators for Safety, Risk, Availability and Maintenance management with a careful quantifi cation of uncertainties.

• Industry 4.0 and predictive maintenance

In the framework of Industry 4.0, our main contribution is to develop advanced models and optimization methods for dynamic risk management and predictive maintenance. This encompasses the assessment and modelling of components degradation with system usage, and the analyze and optimization of maintenance solutions. This can be done by multi-state physics, Bayesian and Markov chains models, Monte Carlo simulation. A particular focus is on failure prediction and prognostics of critical components, by data-driven approaches, e.g. adaptive artifi cial neural networks, support vector machines and the like. Regarding optimization, different methods are implemented in relation with optimization under uncertainties and robust optimization.

• Resilience

We intend to assess and optimize the resilience of complex systems and critical infrastructures by modeling and optimizing the processes of barrier management, mitigation, crisis management, recovery. One of the objectives is to guaranty business continuity by investigating in which degraded states the system should be put back in a minimal amount of time. The approaches developed are related to agent based modelling and resilient communities.

Our teaching activity is organized around 4 master level courses:

• Risk assessment and resilience of systems and infrastructures

• Practical risk and reliability analysis

• Engineering maintenance

• Mathematical models for decision making

• Data analysis for risk and reliability

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Risk Resilience Reliability Research Group

Risks and Resilience of complex System Chair


Head : Anne BARROS
Tel: +33 (0)1 75 31 69 14

Administration: Delphine Martin
Tel:+33(0)1 75 31 63 88
Laboratoire Genie Industriel
3, rue Joliot Curie
91192 Gif-sur-Yvette

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