Machinery Vibration Signals Analysis and Monitoring for Fault Diagnosis and Process Control

  • Authors:

  • Juan Dai;C. L. Chen;Xiao-Yan Xu;Ying Huang;Peng Hu;Chi-Ping Hu;Tao Wu

  • Affiliations:

  • The Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China 650091;The Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, USA TX 78249;The Electrical Engineering Department, Shanghai Maritime University, Shanghai, China 200135;Yunnan University of Traditional Chinese Madicine, Kunming, China 650021;The Faculty of Applied Technology, Kunming University of Science and Technology, Kunming, China 650091;The Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China 650091;The Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China 650091

  • Venue:
  • ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
  • Year:
  • 2008

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Abstract

The vibration signals contain a wealth of complex information that characterizes the dynamic behavior of the machinery. Monitoring rotating machinery is often accomplished with the aid of vibration sensors. Transforming this information into useful knowledge about the health of the machine can be challenging due to the presence of extraneous noise sources and variations in the vibration signal itself. This paper describes applying vibration theory to detect machinery fault, via the measurement of vibration and voice monitoring machinery working condition, also proposes a useful way of vibration analysis and source identification in complex machinery. An actual experiment case study has been conducted on a mill machine. The experiment results indicate that fewer sensors and less measurement and analysis time can achieve condition monitoring, fault diagnosis, and damage forecasting. Further applications allow feedback to the process control on production line.