Automatic condition monitoring system for crack detection in rotating machinery

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dc.contributor.author Gómez García, María Jesús
dc.contributor.author Castejón Sisamón, Cristina
dc.contributor.author García Prada, Juan Carlos
dc.date.accessioned 2021-07-01T08:56:20Z
dc.date.available 2021-07-01T08:56:20Z
dc.date.issued 2016-08
dc.identifier.bibliographicCitation Gómez, M., Castejón, C. & García-Prada, J. (2016). Automatic condition monitoring system for crack detection in rotating machinery. Reliability Engineering & System Safety, vol. 152, pp. 239–247.
dc.identifier.issn 0951-8320
dc.identifier.uri http://hdl.handle.net/10016/32968
dc.description.abstract Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the Wavelet Packets transform energy combined with Artificial Neural Networks with Radial Basis Function architecture (RBF-ANN) are applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from a rig where the shaft rotates under its own weight, at steady state at different crack conditions. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). The parameters for Wavelet Packets transform and RBF-ANN are selected to optimize its success rates results. Moreover, ‘Probability of Detection’ curves were calculated showing probabilities of detection close to 100% of the cases tested from the smallest crack size with a 1.77% of false alarms.
dc.description.sponsorship The authors would like to thank the Spanish Government for financing through the CDTI project RANKINE21 IDI-20101560.
dc.format.extent 9
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2016 Elsevier Ltd.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Cracked shaft detection
dc.subject.other Wavelet transform
dc.subject.other Intelligent classification systems
dc.subject.other Condition monitoring
dc.subject.other Artificial neural networks
dc.title Automatic condition monitoring system for crack detection in rotating machinery
dc.type article
dc.subject.eciencia Ingeniería Mecánica
dc.identifier.doi https://doi.org/10.1016/j.ress.2016.03.013
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. IDI-20101560
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 239
dc.identifier.publicationlastpage 247
dc.identifier.publicationtitle Reliability Engineering & System Safety
dc.identifier.publicationvolume 152
dc.identifier.uxxi AR/0000017915
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
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