Residual generation and visualization for understanding novel process conditions

dc.contributor.author	Díaz Blanco, Ignacio
dc.contributor.author	Hollmen, J.
dc.date.accessioned	2014-02-24T10:41:54Z
dc.date.available	2014-02-24T10:41:54Z
dc.date.issued	2002
dc.identifier.uri	http://hdl.handle.net/10651/22846
dc.identifier.uri	http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?arnumber=1007460&tag=1
dc.description.abstract	We study the generation and visualization of residuals for detecting and identifying unseen faults using auto-associative models learned from process data. Least squares and kernel regression models are compared on the basis of their ability to describe the support of the data. Theoretical results show that kernel regression models are more appropriate in this sense. Moreover, experiments on vibration and current data from an asynchronous motor confirm the theory and yield more meaningful results
dc.format.extent	p. 2070-2075
dc.language.iso	eng
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks, 3
dc.rights	© IEEE
dc.rights	CC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title	Residual generation and visualization for understanding novel process conditions
dc.type	conference output	spa
dc.identifier.doi	10.1109/IJCNN.2002.1007460
dc.rights.accessRights	open access

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