Sensor Fault Detection and Isolation of an Air Quality Monitoring Network Using Nonlinear Principal component Analysis
Résumé
Recently, fault detection and process monitoring using principal component analysis (PCA) were studied intensively and largely applied to industrial process. PCA is the optimal linear transformation with respect to minimizing the mean squared prediction error. If the data have nonlinear dependencies, an important issue is to develop a technique which can take into account this kind of dependencies. Recognizing the shortcomings of PCA, a nonlinear extension of PCA is developed. This paper proposes an application for sensor failure detection and isolation (FDI) to an air quality monitoring network via nonlinear principal component analysis (NLPCA). The NLPCA model is obtained by using two cascade three layer RBF-Networks. For training these two networks separately, the outputs of the first network are estimated using principal curve algorithm [7] and the problem is transformed as two nonlinear regression problems.