A model based fault detection and diagnosis system for rolling mill equipments

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dc.contributor.authorOrchard, M. C.
dc.contributor.authorCipriano, A. M.
dc.contributor.authorCipriano, Aldo
dc.contributor.authorViale, M.
dc.contributor.authorVigliocco, A.
dc.date.accessioned2022-05-11T20:05:40Z
dc.date.available2022-05-11T20:05:40Z
dc.date.issued2001
dc.description.abstractIn this paper, the implementation of a Model Based Fault Detection and Diagnosis System, that uses fuzzy logic to determinate the nature of the detected faults in rolling mill equipments is presented. The system is built with 4 components which work independently. An Identification module estimates the parameters of a continuous domain second order transfer function model for the process by analyzing the step response. A Predictive model module generates the controlled variable residual which is statistically analyzed in a Detection module. The results of the statistical analysis are fuzzified and processed in a Diagnosis module to determine detected fault's nature. The system is tested using real operation data of a main motor process in order to detect and classify abnormalities into Operation Point Change (OPC) or Process Fault (PF) alarms.
dc.fuente.origenIEEE
dc.identifier.doi10.23919/ECC.2001.7075954
dc.identifier.isbn978-3952417362
dc.identifier.urihttps://doi.org/10.23919/ECC.2001.7075954
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7075954
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63743
dc.information.autorucEscuela de ingeniería ; Cipriano Zamorano, Aldo ; S/I ; 99102
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherIEEE
dc.relation.ispartofEuropean Control Conference (2001 : Porto, Portugal)
dc.rightsacceso restringido
dc.subjectFault detection
dc.subjectMathematical model
dc.subjectFault diagnosis
dc.subjectEquations
dc.subjectPredictive models
dc.subjectStandards
dc.subjectTransfer functions
dc.titleA model based fault detection and diagnosis system for rolling mill equipmentses_ES
dc.typecomunicación de congreso
sipa.codpersvinculados99102
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