Fault diagnosis model based on fuzzy support vector machine combined with weighted fuzzy clustering

Abstract

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC). Considering the relationship between the sample point and non-self class, FC algorithm is applied to generate fuzzy memberships. In the algorithm, sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC. Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm, the penalty factor and kernel parameter of which are optimized by GA. Finally, the model is executed for multi-class fault diagnosis of rolling element bearings. The results show that the presented model achieves high performances both in identifying fault types and fault degrees. The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.