Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealin...

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Veröffentlicht in:	IEEE access 2020, Vol.8, p.181976-181987
Hauptverfasser:	Xiong, Jianbin, Liu, Xi, Zhu, Xingtong, Zhu, Hongbin, Li, Haiying, Zhang, Qinghua
Format:	Artikel
Sprache:	eng
Schlagworte:	Cluster analysis Clustering Clustering algorithms Fault detection Fault diagnosis fuzzy c-means clustering algorithm genetic algorithm Genetic algorithms Indexes Machine learning Machinery mutual dimensionless indexes Outliers (statistics) Rotating machinery Sensitivity Simulated annealing simulated annealing algorithm Simulation Time-domain analysis
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