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Selection of the level of vibration signal decomposition and mother wavelets to determine the level of failure severity in spur gearboxes.

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dc.contributor.author Perez-Torres, Antonio es_ES
dc.contributor.author Sanchez, Rene-Vinicio es_ES
dc.contributor.author Barceló-Cerdá, Susana es_ES
dc.date.accessioned 2025-02-26T19:09:45Z
dc.date.available 2025-02-26T19:09:45Z
dc.date.issued 2024-10 es_ES
dc.identifier.issn 0748-8017 es_ES
dc.identifier.uri http://hdl.handle.net/10251/214868
dc.description.abstract [EN] Spur gearboxes are an integral component in the operation of rotary machines.Hence, the early determination of the severity level of a failure is crucial. This manuscript delineates a methodology for selecting essential mother wavelets and filters from the wavelet transform (WT) to process the vibration signal within the time-frequency domain, aiming to ascertain the severity level of failures in spur gearboxes. Initially, information is garnered from the gearbox through vibration signals in the time domain, utilising six accelerometers. Subsequently, the signal is partitioned into various levels, and information from each level is extracted using diverse mother wavelets and their respective filters. The signal is segmented into sub-bands, from which the condition state is ascertained using an energy operator. After that, the appropriate level of wave decomposition is determined through ANOVA tests and post-hoc Tukey analyses, evaluating performance in failure classification via the Random Forest (RF) model. Upon establishing the decomposition level, the analysis proceeds to identify which mother wavelets and filters are most suitable for determining the severity level of different types of failure in spur gearboxes. Moreover, this study investigates the impact of sensor positioning and inclination on acquiring the vibration signal. This aspect is explored through factorial ANOVA tests and multiple comparisons of the data derived from the sensors. The RF classification model achieved exceedingly favourable results (accuracy >96% and AUC >98%), with minimal practical influence from the positioning and inclination of a sensor, thereby affirming the proposed methodology¿s suitability for this type of analysis. es_ES
dc.description.sponsorship Universidad Politecnica Salesiana,Grant/Award Number: 021-001-2020-01-23 es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof Quality and Reliability Engineering International es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Classification models es_ES
dc.subject Feature extraction es_ES
dc.subject Fault severity es_ES
dc.subject Time-frequency domain es_ES
dc.subject Wavelet packets transform es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Selection of the level of vibration signal decomposition and mother wavelets to determine the level of failure severity in spur gearboxes. es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/qre.3578 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPS//021-001-2020-01-23/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural es_ES
dc.description.bibliographicCitation Perez-Torres, A.; Sanchez, R.; Barceló-Cerdá, S. (2024). Selection of the level of vibration signal decomposition and mother wavelets to determine the level of failure severity in spur gearboxes. Quality and Reliability Engineering International. 40(6):3439-3451. https://doi.org/10.1002/qre.3578 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1002/qre.3578 es_ES
dc.description.upvformatpinicio 3439 es_ES
dc.description.upvformatpfin 3451 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 40 es_ES
dc.description.issue 6 es_ES
dc.relation.pasarela S\519005 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Universidad Politécnica Salesiana, Ecuador es_ES


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