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Fault diagnosis using PCA-Bayesian Network classifier with unknown faults

In this paper we propose an original framework for fault diagnosis with Bayesian networks. The proposed approach uses conditional Gaussian network to improve principal component analysis scheme for fault diagnosis. The proposed method allows us to consider known and unknown class of faults. Performance evaluation using the Tennessee Eastman Process data is presented and shows a superior performance of the proposed method compared to existing methods.