Detection, isolation and fault estimation of nonlinear systems using a directional study

12th European Workshop on Advanced Control and Diagnosis

Kallas, Maya^1 ; Mourot, Gilles^1 ; Maquin, Didier^1 ; Ragot, José^1

Centre de Recherche en Automatique de Nancy, CNRS, Université de Lorraine, 2 Avenue de la Forêt de Haye TSA 60604, Vandoeuvre-lès-Nancy

54 518, France^1

关键词: Euclidean distance;    Fault estimation;    Nonlinear functions;    Radial basis functions;    System diagnosis;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012032/pdf
DOI  :  10.1088/1742-6596/659/1/012032

来源: IOP

【 摘 要 】

In terms of system diagnosis, several studies are generally performed. The diagnosis is composed of three different parts: detecting, isolating and estimating the value of the faults. If many results have been obtained for linear systems with a known model, the situation is quite different in the case of nonlinear systems behavior, especially when the model is not known a priori. This paper proposes to discuss the latter case using a study of the dissimilarities between data. The dissimilarities are evaluated by a nonlinear function of the Euclidean distances. To this end, a radial basis function is used, and a directional study is introduced for fault diagnosis. The relevance of the proposed technique is illustrated on simulated data.