Nathalie Pessel

Neuronal principal component analysis for the diagnosis of a non linear system

This paper present a detection and diagnosis sensor faults based on a Neuronal Non Linear Principal Component Analysis (NNLPCA) and on a Fisher Discriminant Analysis (FDA). This method is validated in simulation on a non linear system: an experimental greenhouse. Several results are presented. The neuronal approach of the NLPCA is used to underline the correlations between the variables and to estimate the non linear principal components. This NLPCA model allows to estimate the prediction error (SPE: Squared Prediction Error) and to define data classes with and without fault. The classes associated to data with fault are isolated by applying a FDA.

A Multiple Sensor Fault Detection Method based on Fuzzy Parametric Approach

This paper presents a new approach for the model-based diagnosis. The model is based on an adaptation with a variable forgetting factor. The variation of this factor is managed thanks to fuzzy logic. Thus, we propose a design method of a diagnosis system for the sensor defaults. In this study, the adaptive model is developed theoretically for the Multiple-Input Multiple-Output (MIMO) systems. We present the design stages of the fuzzy adaptive model and we give details of the Fault Detection and Isolation (FDI) principle. This approach is validated with a benchmark: a hydraulic process with three tanks. Different defaults (sensors) are simulated with the fuzzy adaptive model and the fuzzy approach for the diagnosis is compared with the residues method. The method is efficient to detect and isolate one or more defaults. The results obtained are promising and seem applicable to a set of MIMO systems.