Neural Computing and Applications

, Volume 18, Issue 5, pp 447–453 | Cite as

High-voltage equipment condition monitoring and diagnosis system based on information fusion

ISNN 2008

Abstract

As high-voltage electric equipment has complex structure and works in harsh environments, fiber Bragg grating (FBG) sensors are applied to realize the real-time monitoring of some parameters in which temperature is the main parameter. Using FBG sensors to monitor temperature of high-voltage electric equipment can overcome the disadvantages of harsh monitoring environment such as high-voltage, big current, strong electromagnetic interference and so on. The fault of high-voltage electric equipment is difficult to be distinguished as there may be many different reasons. The traditional or simple methods cannot totally meet the demand of fault diagnosis of high-voltage electric equipment. First, taking neural network as a classifier to distinguish different fault types from complex fault information in the feature layer can supply a good foundation to final information fusion diagnosis. Second, Dempster–Shafer evidence theory is used to make a comprehensive diagnosis of fault information in the decision layer. All the uses above can increase the speed and accuracy of diagnosis and have practical significance. The fault diagnosis system shows good results and provides an effective way to realize the real-time condition monitoring and more accurate fault diagnosis of high-voltage electric equipment.

Keywords

Information fusion Condition monitoring Fault diagnosis FBG sensing system Neural network 

Notes

Acknowledgments

This article was supported by the Natural Science Foundation of China (Grant No. 60674107), the Natural Science Foundation of Hebei Province (Grant No. F2006000343), and the Research and Development Plan of Science and Technology of Shijiazhuang (No. 06713026A).

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Yongwei Li
    • 1
  • Zhenyu Wang
    • 1
    • 2
  • Xingde Han
    • 1
  • Yalun Li
    • 1
  1. 1.College of Electrical and Information EngineeringHebei University of Science and TechnologyShijiazhuangChina
  2. 2.College of AutomationBeijing Institute of TechnologyBeijingChina

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