Research on a New Hybrid Intelligent Fault Diagnosis Method and its Application

다국어 초록 (Multilingual Abstract)

In order to overcome the shortcomings of slow convergence speed and easy falling into the local minimum values of the BP neural network, an improved particle swarm optimization(PSO) algorithm is proposed to optimize the redial basic function (RBF) neural network, in order to propose a new hybrid intelligent fault diagnosis(IMPSO-RBFNN) method. In the IMPSO-RBFNN method, the adaptive dynamic adjusting strategy is used to control the inertia weight of the PSO algorithm in order to an improved particle swarm optimization(IMPSO) algorithm. Then the IMPSO algorithm is selected to optimize the parameters of RBF neural network by encoding the particle and continuous iteration of the IMPSO algorithm in order to obtain the optimal combination values of the parameters of RBF neural network. The optimal combination values are regarded as the values of these parameters of the RBFNN for constructing the final IMPSO-RBFNN method. In order to test the effectiveness of the proposed IMPSO-RBFNN method, the data from bearing data center of CWRU is selected in this paper. The experiment results show that the IMPSO algorithm can effectively optimize the weights of RBFNN, the IMPSO-RBFNN method can accurately realize high precision fault diagnosis of rolling bearing.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. RBF Neural Network
• 3. Improved Particle Swarm Optimization (PSO) Algorithm
• 3.1. Particle Swarm Optimization (PSO) Algorithm
• 3.2. Improved PSO Algorithm
• 4. A New Hybrid Intelligent Fault Diagnosis Method
• 5. Fault Diagnosis Case for Bearing of Motor
• 6. Conclusion
• Acknowledgments
• References