Reseach Topics

DATA VALIDATION

The purpose of data validation and reconciliation include the following topics:

- detect and correct deviations and errors of measurement data so that these satisfy all balance constraints

- exploit the structure and the knowledge of the process system together with the measurement data to compute unmeasured data wherever it is possible

- determine the post-processing accuracy of measured and unmeasured data.

Keywords : data reconciliation, mass balance equilibration, estimation, observability analysis, data redondancy, sensors positionning, reliability, robustness, contaminated distributions, bounded errors.

MODELISATION and SYSTEM IDENTIFICATION

In order to establish a methodology for modelisation applicable in a wide class of industrial plants, we can observe that in many cases the processes can be described using simple model having a local validity around an operating condition. Therefore, instead of exploiting complicated non-linear models obtained by modelling techniques, it is also possible to describe the plant by a collection of affine models. Each submodel approximates the system locally around a working condition and a selection procedure determines which particular submodel has to be used. Such a multimodel structure will be called multiple model approach. At each operating point, the behaviour of the multiple model is described by a local affine dynamic model.

Keywords : fuzzy models, multi-models with coupling states, multi-models with uncoupled states, robust identification.

SYSTEM DIAGNOSIS AND CONTROL

The key idea of model-based approaches for Fault Detection is, in fact, the generation of signals, termed residuals, obtained by using observers, parameter estimation or parity equations designed on the basis of mathematical models of the monitored system. Residual are signals representing inconsistencies between the model and the actual system being monitored. Any inconsistency will indicate a fault in the system. Residual must, therefore, be different from zero when a fault occurs and zero otherwise. However, the deviation between the model and the plant is influenced not only by the presence of the fault but also the modelling error. Instead of exploiting complicated non-linear models obtained by modelling techniques, the problem is overcome describing the plant by a collection of local linear models obtained by the non-linear identification method presented above.

Keywords : residuals generation, state and output observers, residuals structuration, parity space, fault detection and localisation, unknown input observers, systems with uncertain parameters, fault tolerant control, observers for multi-models, principal component analysis