earticle

논문검색

Extension Neural Network Learning Algorithms and Models and their Applications in Fault Diagnosis of Rolling Bearing

원문정보

초록

영어

Extension neural network is a new type of neural network that combines extension theory and artificial neural network. Extension neural network has been applied to pattern recognition, fault diagnosis and clustering. According to fault characteristics of rolling bearing, we propose a fault diagnostic method for rolling bearing based on extension neural network. We construct the fault diagnosis model based on extension neural network along with the learning algorithm, which are then applied to fault recognition of rolling bearing. Simulation experiment indicates that this algorithm is easy to implement and has small training error and fast convergence speed. The algorithm has both theoretical and practical value.

목차

Abstract
 1. Introduction
 2. Fault Diagnostic Methods for Rolling Bearing
 3. Extension Neural Network
 4. Simulation Experiment and Analysis
  4.1. Data Processing
  4.2. Network Training
  4.3. Comparative Analysis
 5. Conclusion
 References

저자정보

  • Zhang Su College of Mechanical and Electrical Engineering, Agriculture University of Hebei, Baoding, Hebei, China
  • Zheng Ying College of Mechanical and Electrical Engineering, Agriculture University of Hebei, Baoding, Hebei, China

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.