A comparison of fuzzy clustering algorithms for bearing fault diagnosis

Chuan Li, Mariela Cerrada, Diego Cabrera, René Vinicio Sanchez, Fannia Pacheco, Gözde Ulutagay, José Valente De Oliveira

Resultado de la investigación: Contribución a una conferenciaDocumento

72 Citas (Scopus)

Resumen

© 2018 - IOS Press and the authors. All rights reserved. Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays an important role as it can be used in fault detection, classification, and prognosis. A variety of clustering algorithms have been proposed and applied in this context. However, when the extensive literature on this topic is investigated, it is not clear which clustering algorithm is the most suitable, if any. In an attempt to bridge this gap, in this study four representative fuzzy clustering algorithms are compared under the same experimental realistic conditions: fuzzy c-means (FCM), the Gustafson-Kessel algorithm, FN-DBSCAN, and FCMFP. The study considers only real-world bearing vibration data coming from both a benchmark data set (CWRU) and from a lab setup where interference between bearing faults can be studied. The comparison takes into account the quality of the generated partitions measured by the external quality (Rand and Adjusted Rand) indexes. The conclusions of the study are grounded in statistical tests of hypotheses.
Idioma originalInglés
Páginas3565-3580
Número de páginas16
DOI
EstadoPublicada - 1 ene. 2018
EventoJournal of Intelligent & Fuzzy Systems - , Países Bajos
Duración: 1 ene. 1996 → …

Conferencia

ConferenciaJournal of Intelligent & Fuzzy Systems
País/TerritorioPaíses Bajos
Período1/01/96 → …

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