Earticle

초록

영어

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

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

키워드

저자

• Zhenhua Wang [ College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, China ]
• Zhentao Liu [ College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, China ]
• Xueyan Lan [ College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, China ]
• Jian Liu [ College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, China ]
• Shaowei Wang [ College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, China ]
• Yangming Wu [ College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, China ]
• Yanbing Xue [ College of Electrical and Information, Dalian Jiaotong University, Dalian 116028, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605