@article{dangelo_new_2016,
	title = {A new fault classification approach applied to {Tennessee} {Eastman} benchmark process},
	volume = {49},
	issn = {1568-4946},
	url = {https://www.sciencedirect.com/science/article/pii/S1568494616304343},
	doi = {10.1016/j.asoc.2016.08.040},
	abstract = {This study presents a data-based methodology for fault detection and isolation in dynamic systems based on fuzzy/Bayesian approach for change point detection associated with a hybrid immune/neural formulation for pattern classification applied to the Tennessee Eastman benchmark process. The fault is detected when a change occurs in the signals from the sensors and classified into one of the classes by the immune/neural formulation. The change point detection system is based on fuzzy set theory associated with the Metropolis–Hastings algorithm and the classification system, the main contribution of this paper is based on a representation which combines the ClonALG algorithm with the Kohonen neural network.},
	language = {en},
	urldate = {2022-05-02},
	journal = {Applied Soft Computing},
	author = {D’Angelo, Marcos F. S. V. and Palhares, Reinaldo M. and Camargos Filho, Murilo C. O. and Maia, Renato D. and Mendes, João B. and Ekel, Petr Ya.},
	month = dec,
	year = {2016},
	keywords = {Fault detection and isolation, Fuzzy/Bayesian approach, Immune/neural formulation, Tennessee Eastman benchmark process},
	pages = {676--686},
}

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