Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Stable local computation with conditional Gaussian distributions
Statistics and Computing
FDI in Multivariate Process with Naive Bayesian Network in the Space of Discriminant Factors
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Fault diagnosis using dynamic trend analysis: A review and recent developments
Engineering Applications of Artificial Intelligence
Belief update in CLG Bayesian networks with lazy propagation
International Journal of Approximate Reasoning
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
Engineering Applications of Artificial Intelligence
Fault detection and isolation for PEM fuel cell stack with independent RBF model
Engineering Applications of Artificial Intelligence
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The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with conditional Gaussian network (CGN). The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a CGN in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault.