Online fault detection and isolation of an AIR quality monitoring network based on machine learning and metaheuristic methods

The dynamic process monitoring is discussed in this paper. Kernel principal component analysis (KPCA) is a nonlinear monitoring method that cannot be applied for dynamic systems. Reduced online KPCA (OR-KPCA)is used for fault detection of dynamic processes, which is developed to built a dictionary according to the process status and then, it update the KPCA model and uses it for process monitoring. Also the Tabu search metaheuristic algorithm is used in order to determine the optimal parameter of the kernel function. In this paper, new approaches for online fault isolation, which is a challenging problem in nonlinear PCA, are formulated. An extension of partial PCA and the elimination sensor identification (ESI) to the case of nonlinear systems are presented in a feature space. The partial OR-KPCA and the elimination sensor identification (ESI-KPCA) are generated based on the OR-KPCA method and they consist of developing a set of sub-models. The sub-models are selected according to a pre-designed fault-to-residual structure matrix and by eliminating sequentially one variable from the set of the variables. The proposed fault isolation methods are applied for monitoring an air quality monitoring network. The simulation results show that the proposed fault isolation methods are effective for KPCA.