Rolling bearing fault diagnosis of SVM optimized by surface-simplex swarm evolution
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
To solve the problem that the parameter optimization algorithm of support vector machine (SVM) has many control parameters and easily falls into local optimum, a rolling bearing fault diagnosis method of SVM optimized by surface-simplex swarm evolution algorithm is proposed. First, surface-simplex swarm evolution (SSSE) is used to establish the particle's simple neighborhood search operator in a random way to reduce control parameters and develop the multi-role state search strategy to avoid falling into the local optimum. Then, SSSE is applied to parameter optimization of SVM to realize fault diagnosis. In the test, the energy matrix of ensemble empirical mode decomposition (EEMD) of rolling bearing signal is used as the feature input to perform performance analysis and testing of the method. The results show that the method not only effectively avoids the particles falling into the local optimal solution, but also reduces the control parameters, and can complete the signal diagnosis.
