Earticle

초록

영어

The difficulty of CNC machine tools fault diagnosis is bigger than other general equipments because of the complex structure and the coupling among subsystems. The fault diagnosis model based on multi-level information fusion and hybrid intelligence is studied to improve reliability of fault diagnosis. Information from built-in sensors is used to monitor the status of CNC machine tools. The diagnosis principles of internal parameters-motor current, torque, temperature and following error are analyzed. Internal information and external sensors are two main sources which provide data to diagnosis. In order to detect effective fault signal, features of time domain, frequency domain and time-frequency domain are extracted. All these features constitute the feature set. The features are selected by the method of Kernel Principal Component Analysis (KCPA). Then the sensitive feature set is obtained. The method of multiple classifier fusion based on fuzzy comprehensive evaluation is researched. The determination method of weight based on information entropy is proposed. This diagnosis model has been tested feed system mechanical fault diagnosis of CNC machine tools and the results show which is effective and versatile.

목차

Abstract
 1. Introduction
 2. Diagnosis Model Based on Information Fusion and Hybrid Intelligence
 3. Condition Monitoring System Based on Multi-Dimension Information
 4. Diagnosis Principles of Internal Parameters
  4.1. Motor Torque
  4.2. Temperature of Motor
  4.3. Following Error of Shaft
 5. Multi-Domain Mixing Features
 6. Feature Selection Based on Kernel Principal Component Analysis
 7. Multiple Classifiers Fusion Based on Fuzzy Comprehensive Evaluation
 8. Experiment
 9. Conclusions
 Acknowledgements
 References

키워드

저자

• Wen Yan [ Department of Mechanical Engineering, Qingdao Technological University, Qingdao 266052, China ]
• Tan Ji-wen [ Department of Mechanical Engineering, Qingdao Technological University, Qingdao 266052, China ]
• Zhan Hong [ Department of Mechanical Engineering, Qingdao Technological University, Qingdao 266052, China ]
• Sun Xian-bin [ Department of Mechanical Engineering, Qingdao Technological University, Qingdao 266052, 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