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

• Published in: IEEE access Vol. 8; pp. 181976 - 181987
• Main Authors: Xiong, Jianbin, Liu, Xi, Zhu, Xingtong, Zhu, Hongbin, Li, Haiying, Zhang, Qinghua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3021720

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 annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. Compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery.