Earticle

초록

영어

Based on the analysis of the defect of traditional model, this paper proposes a new control chart pattern recognition model, which includes Wavelet Analysis (WA), Principal Component Analysis (PCA), Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). WA is good to eliminate noise control chart anomaly pattern recognition of the adverse effect. PCA eliminates the redundant information of data between SVM and reduces the input dimension and computational complexity. PSO algorithm optimizes the parameters of SVM and the establishment of the optimal control chart anomaly pattern classifier can solve the problem optimal parameters of SVM. The simulation results show that the model is feasible, the results are reliable. This algorithm improves the control chart abnormal state average recognition accuracy and be used in the machining process real-time monitoring.

목차

Abstract
 1. Introduction
 2. Control Chart Pattern Primitive Type and Recognition Principle
 3. Hybrid Patterns Recognition of Control Chart Framework
  3.1. WA Eliminates Noise of the Control Chart Time Series Data
  3.2. PCA Reduces Time Series Data Dimension of Control Chart
  3.3. Support Vector Machine Classify the Control Chart
 4. Simulation Results and Application
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Liu Yan-zhong [ College of Computer and Control Engineering, Qiqihar University, Qiqihar, China ]
• Zhang Hong-lie [ College of Computer and Control Engineering, Qiqihar University, Qiqihar, China ]
• Liu Yan-ju [ Computing Center, Qiqihar University, Qiqihar, China ]
• Jiang Jin-gang [ College of Mechanical & Power Engineering, Harbin University of Science and Technology, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605