Some preliminary results on the comparison of FCM, GK, FCMFP and FN-DBSCAN for bearing fault diagnosis

Abstract:

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, this paper reports some preliminary results of a work aiming at comparing four representative fuzzy clustering algorithms: fuzzy c-means (FCM), the Gustafson-Kessel (GK) algorithm …

Año de publicación:

2017

Keywords:

Fuente:

google