Two amazing PhD opportunities from Prof Didier Remond, Lyon Unversity, are now available for application.
The PhD positions are part of the “MOIRA” (MOnItoRing of large scale complex technologicAl systems)
project, funded by the European Commission through the H2020 “Marie Skłodowska-Curie Innovative
Training Networks” program (grant number 955681).
Project 1: Probabilistic fleet monitoring based on model manifold
Within the context of the MOIRA project, the PhD student will develop model-based techniques for monitoring individual units in a fleet from a probabilistic approach. The principles is based on the comparison of a parametric model identified from measured data to a generic model provided by dynamic modelling by taking in account acceptable differences between the generic model of the fleet and individual units. This will be solved as an inverse problem in the presence of “modelling errors” that reflect the departure of individual units from the nominal model representing the fleet. After identification of the model parameters from experimental data – a non-trivial issue due to the fact that modelling errors are highly structured and do not comply with the simplifying assumption commonly advocated in system identification theory — each unit will be assigned a different realization of the same model seen as a random object. This will be formalized in the probabilistic space spanned by the parameter values, thus leading to the concept of a population of models embedded in a probabilistic manifold endowed with a topological information geometry. This formalism will be useful to view and manipulate models as probabilistic objects.
The objective for monitoring will then be to assess whether the dispersion attached to a specific unit is due to normal variability or to the presence of an abnormality. In addition, the dynamic evolution in time of models will be accounted for (presence of confounding environmental variables, dependence on operational conditions) as trajectories in the probabilistic manifold. Therefore, a Bayesian probabilistic approach will be developed in order to
- 1) account for all elements of information that are a priori available on modelling errors,
- 2) propagate them in the inverse problem, and
- 3) set up credible intervals used in the ultimate diagnostic step.
Monte Carlo Markov Chains will be used as the machinery to solve these objectives, as they make possible to jointly infer the model parameters and the modelling errors characteristics. The methodology will be applied on fleets of vehicles and wind turbines.
Project 2: Improving virtual sensing by multi-complexity models
Within the context of the MOIRA project, the PhD student will work on fleet monitoring, using a “generic model” and solving an inverse identification problem. In the case of an heterogeneous fleet of machines, the generic model has to face different levels of complexity or various phenomenological contents to describe correct dynamic behaviours of units. If one looks for sharing information between these units of the fleet, it is necessary to have common signals which may be virtual and not necessary physical. The choice and the use of different models that map the physical quantities of interest to the measured signals will be investigated in order to improve the general performances of a “virtual sensor”, which is achieved through inverse modelling. The richness of the sensor equipment of a unit (machine, vehicle etc.) will be used in order to correlate the virtual signal estimations from different inverse model architectures. Methodologies will be proposed for the identification of the particular operating conditions of each unit, updating simultaneously their estimation by sharing reconstructed signals of the fleet units.
Considering the operating conditions and architectures of each unit in the fleet, it is necessary to consider behavioural models with different levels of complexity but which share identical signals to increase the efficiency of detection of abnormal situations. This can be considered as a “virtual machine” with its generic model identified on the basis of existing signals.
This work will be based on recent identification techniques and will benefit from an a priori knowledge of excitation components (bearings, gears, electrical machines, etc.). The general framework of angular approaches will also be used to describe the transfer path from known excitation components to physical signals (used for model identification and fault detection) and to virtual signals (used for fleet comparison between units and fault detection) in these machine architectures. In particular, nonstationary operating conditions will be investigated to extend opportunities of better identification performances and opportunities for detection of abnormal situations. The methodologies will be applied to fleets of vehicles and wind turbines and/or of some specific components of such machines like drive trains or power transmission units.