Reliability engineering & measurement methods
Data-driven approaches are currently transforming the modeling and analysis of complex systems. This phenomenon can be explained by an increasing access to large databases, high computational capacities, even on standard machines, and the rise of accurate prediction capabilities of learning algorithms, typically neural networks and their variants. The industrial field benefits directly from this through connected machines that can be easily controlled remotely and whose multiple sensors allow the evaluation of the operating status in real time. These novel aspects are referred to as Industry 4.0. They offer new possibilities in reliability engineering, in particular for the functional safety and the maintenance of complex technical systems.
The sensor revolution also hits the traffic engineering. The arrival of large amounts of individual vehicular data opens up new ways of understanding and modelling vehicle dynamics. It also calls for the development of new data processing methods. Numerous applications exist, for the estimation of performance and safety levels of traffic flows or the estimation of pollutant emission rates and energy consumption.
