A comparison of fuzzy clustering algorithms for bearing fault diagnosis

作者：Li, Chuan; Cerrada, Mariela; Cabrera, Diego; Sanchez, Rene Vinicio; Pacheco, Fannia; Ulutagay, Gozde; de Oliveira, Jose Valente^*

来源：Journal of Intelligent and Fuzzy Systems, 2018, 34(6): 3565-3580.

DOI：10.3233/JIFS-169534

• Bearing
• fault detection
• fault diagnosis
• fault classification
• fuzzy rules
• fuzzy clustering
• FCM
• Gustafson-Kessel clustering
• FCMFP
• FN-DBSCAN

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.

• 出版日期2018
• 单位重庆工商大学