Earticle

초록

영어

In allusion to the low correctness and efficiency of fault diagnosis for the complex industrial system, rough set theory, particle swarm optimization and back propagation (BP) neural network are introduced to propose a hybrid intelligent fault diagnosis(RPBPNN) method in this paper. In the proposed RPBPNN method, rough set theory as a new mathematical tool is used to process inexact and uncertain knowledge in order to obtain the minimum fault characteristic set for simplifying the structure and improving learning efficiency of BPNN. The particle swarm optimization (PSO) algorithm with the global optimization ability is directly used to train the weights of BP neural network in order to establish the optimized BP neural network model. Then the minimum fault characteristic set is used to train the optimized BP neural network model in order to obtain the optimal BP neural network model for realizing the fault diagnosis. Finally, the proposed RPBPNN method is applied to an actual application case for verifying the effectiveness. The experimental results show that PSO algorithm can search for the optimal values of BPNN parameters and the proposed RPBPNN method can accurately eliminate false and improve the diagnostic accuracy. So the proposed RPBPNN method takes on better generalization performance and prediction accuracy in the real industrial application system.

목차

Abstract
 1. Introduction
 2. Basic Methods
  2.1. Rough Set Theory
  2.2. Particle Swarm Optimization (PSO) Algorithm
  2.3. BP Neural Network
 3. A Hybrid Intelligent Fault Diagnosis (RPBPNN) Method
  3.1. The Idea of RPBPNN Method
  3.2. The Flow of RPBPNN Method
 4. Analyze Actual Application Case
 5. Conclusion
 References

키워드

저자

• Jian Chu [ Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222 China ]
• Yadong Niu [ Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605