Faults diagnosis and detection using principal component analysis and Kullback-Leibler divergence

Fault Detection and Isolation (FDI) based on Principal Component Analysis (PCA) is achieved through the construction of control charts. Control charts differ, primarily, by the subspace into which they were defined, namely, the principle and the residual subspaces. Abnormalities are detected in the plotted monitoring chart if the confidence limit is violated. Often, the Hotelling's T 2 control chart, defined in the principal subspace, is applied for process monitoring. But to detect a fault with the T 2 chart, it must cause significant changes in the principal subspace, because little disturbances may be hidden by the large amount of variabilities present in the principal subspace. In this paper, we propose to use the Kullback-Leibler divergence, a probabilistic measure taken from information theory, as a diagnosis criterion. We show the efficiency of this criterion for which we find that small faults which might not be detected by the Hostelling test, become detectable without ambiguity. The simulation results show a significant improvement in the fault detection.