Earticle

초록

영어

In allusion to more indeterminate information and higher speed request characteristic in fault diagnosis system, according to the intelligence complementary strategy, a new fault diagnosis(SWPSO-BPN) model based on combining improved particle swarm optimization (PSO) algorithm and Back-propagation(BP) neural network is proposed in this paper. In the SWPSO-BPN method, an improved PSO (SWPSO) algorithm is proposed to optimize the parameters of BP neural network in order to overcome the shortcomings of slow learning speed and being easy to fall into local minimum, and obtain the optimal values of parameter combination in the BP neural network(SWPSO-BPN) model. Then proposed SWPSO-BPN model is applied to diagnose the fault in order to obtain a new fault diagnosis (SWPSO-BPN-FD) method. Finally, the proposed SWPSO-BPN-FD method is used to test the data from bearing data center of CWRU. The experimental results show that the proposed SWPSO-BPN-FD method can accurately and effectively realize high precision fault diagnosis of rolling bearing. And this method takes on strong robustness and generalization ability.

목차

Abstract
 1. Introduction
 2. The PSO Algorithm and BP Neural Network
  2.1. Particle Swarm Optimization (PSO) Algorithm
  2.2. BP Neural Network
 3. An Improved PSO (ASWPSO) Algorithm
 4. Optimize the BP Neural Network Based on ASWPSO Algorithm
 5. Fault Diagnosis Case Based on ASWPSO-BPN
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Shiwei Li [ School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China ]
• Di Zhou [ Sichuan University of Arts and Science, Dazhou, 635000 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605