Data‐driven approach for fault detection and isolation in nonlinear system

Summary System diagnosis has been of a great interest for all aspects of industrial processes more precisely to gain in quality. It is based essentially on the analysis of the links between the variables of a system and more precisely on the changes of the relations between these variables, which testify to the presence of faults or anomalies. For that purpose, data modeling is the process of finding a mathematical expression that provides a good fit between given finite sample values of the independent variables and the associated values of the dependent variables of the process. The aim of this paper is to detect and, above all, localize faults affecting a system with nonlinear behavior, when its model is not known a priori. An important part of the presentation is dedicated to the construction of fault indicators capable of locating faults, ie, recognizing the input or output of a system affected by a fault. The first part of this paper is devoted to how to predict the output of a nonlinear behavior system. The second part proposes a way for the detection and isolation of measurement faults based on the proposed prediction model. The relevance of the proposed technique, for modeling and system diagnosis, is illustrated on a simulated example in the context of SIMO and MIMO systems.