Automatic diagnoses of rolling bearing failures based in fuzzy logic. / Diagnóstico automático de defeitos em rolamentos baseado em lógica fuzzy

AUTOR(ES)
DATA DE PUBLICAÇÃO

2005

RESUMO

This works describes two proposed methodologies for the automatic diagnoses in mechanical equipment: the fuzzy system inference and a Fuzzy C-Means based algorithm. Their performances are evaluated in an experimental case and, afterwards, also compared by the statistical alarm, a diagnostic methodology very used in industries at present. In order to do the tests, a developed computer algorithm allowed creating alarms and fuzzy systems by the use of an experimental database. These tested diagnostic systems were automatically built using information from the mentioned database that was composed by samples of vibration signals, representing several types of rolling bearing defects and the bearing normal condition. The fuzzy systems input scalar parameters were obtained by signal processing. The influence of some of the building fuzzy systems parameters in the system performance was also studied, which allow establishing, for example, that the database complexity is an important factor in the fuzzy system performance. Finally, this work discusses the main characteristics of each one of the described methodologies. The most important contribution of this work is the proposition of a methodology for creating fuzzy system automatically as well as the analysis of the fuzzy C-Means as a tool for system diagnoses.

ASSUNTO(S)

rolamento fuzzy c-means statistical alarm. fuzzy inference system lógica fuzzy rolling bearing fuzzy c-means alarme estatístico fuzzy logic sistema de inferência fuzzy

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