Earticle

초록

영어

Fault diagnosis for quality control during the multivariate production process is widely used to detect abnormal fluctuations, find out failure reasons and take measures to maintain the stability of the production system accordingly. The neural network methodology has become a main method in the field of intelligent diagnosis recently. However, it has certain deficiencies such as longer training time, slower convergence rate and easier falling into a local optimal solution easily. As a result, the effect of fault diagnose is influenced. Thus, this article proposes the idea of using the Fruit Fly Optimization Algorithm in the multivariable process fault diagnosis model, at the same time, to analyze the out of control sample data in the automobile crankshaft production. Compared with the neural network model in dealing with the fault diagnosis in multivariate process, Fruit Fly Optimization Algorithm’s effectiveness s verified.

목차

Abstract
 1. Introduction
 2. Research Methodology
 3. Presented Multivariate Production Process Failure Diagnosis Algorithm
 4. Case Application
  4.1. Applying FOA for Failure Mode Diagnosis
  4.2. Applying BP Neural Network BP for Failure Mode Diagnosis
  4.3 Diagnosis Results and Comparison
 5. Concluding Remarks and Future Research Directions
 Acknowledgements
 References

키워드

저자

• Yang Mingshun [ Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China ]
• Kong Xiangjian [ Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China ]
• Gao Xinqin [ Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China ]
• Liuyong [ Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China ]
• Li Yan [ Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605