A single Bayesian network classifier for monitoring with unknown classes

A single Bayesian network classifier for monitoring with unknown classes. Atoui, M. A., Cohen, A., Verron, S., & Kobi, A. Engineering Applications of Artificial Intelligence, 85:681–690, October, 2019.

Paper doi abstract bibtex

In this paper, the Conditional Gaussian Networks (CGNs), a form of Bayesian Networks (BN), are used as a statistical process monitoring approach to detect and diagnose faults. The proposed approach improves the structure of Bayesian networks and generalizes a few results regarding statistical tests and the use of an exclusion criterion. The proposed framework is evaluated using data from the benchmark Tennessee Eastman Process (TEP) with various scenarios.

@article{atoui_single_2019,
	title = {A single {Bayesian} network classifier for monitoring with unknown classes},
	volume = {85},
	issn = {0952-1976},
	url = {https://www.sciencedirect.com/science/article/pii/S0952197619301800},
	doi = {10.1016/j.engappai.2019.07.016},
	abstract = {In this paper, the Conditional Gaussian Networks (CGNs), a form of Bayesian Networks (BN), are used as a statistical process monitoring approach to detect and diagnose faults. The proposed approach improves the structure of Bayesian networks and generalizes a few results regarding statistical tests and the use of an exclusion criterion. The proposed framework is evaluated using data from the benchmark Tennessee Eastman Process (TEP) with various scenarios.},
	language = {en},
	urldate = {2021-10-14},
	journal = {Engineering Applications of Artificial Intelligence},
	author = {Atoui, M. Amine and Cohen, Achraf and Verron, Sylvain and Kobi, Abdessamad},
	month = oct,
	year = {2019},
	keywords = {Bayesian networks, Classification, Exclusion criteria, Fault detection and diagnosis},
	pages = {681--690},
}

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