Bearing fault diagnosis based on multiple classifiers group of Fuzzy C Means

In this paper, Fuzzy C-Means (FCM) is adopted to constitute multiple classifiers group to classify the bearing failure and its clustering centers are optimized by Particle Swarm Optimization (PSO) algorithm with global optimization and fast convergence characteristics. Classification recognition rat...

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Bibliographic Details
Main Authors: Li Xinbin, Sun Xi, Chen Yun Qiang
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Clustering algorithms
Educational institutions
Electronic mail
Fault diagnosis
Fuzzy C-Means (FCM)
Fuzzy Integral Information Fusion
Particle swarm optimization
Particle Swarm Optimization (PSO)
Sun
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Summary:In this paper, Fuzzy C-Means (FCM) is adopted to constitute multiple classifiers group to classify the bearing failure and its clustering centers are optimized by Particle Swarm Optimization (PSO) algorithm with global optimization and fast convergence characteristics. Classification recognition rate obtained by FCM is integrated by fuzzy integral information fusion system to gain the final result, in which fuzzy measure also is optimized by the PSO algorithm. Simulation of identifying the bearing inner race, outer race and rolling bodies fault, the results show that the classifier improves the recognition accuracy rate of the fault diagnosis.
ISSN:1934-1768
2161-2927