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, fuzzy c-means with a focal point (FCMFP), and fuzzy neighborhood density-based spatial clustering of applications with noise (FN-DBSCAN). The paper reports only results from the real-world bearing vibration benchmark CWRU data set. The comparison takes into account the quality of the generated partitions measured by the external quality, i.e., Rand index. The conclusions of the study are grounded in statistical tests of hypotheses.