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Neural Computing and Applications

, Volume 31, Supplement 1, pp 337–346 | Cite as

Research on data fusion of multi-sensors based on fuzzy preference relations

  • Huijuan Hao
  • Maoli Wang
  • Yongwei TangEmail author
  • Qingdang Li
Machine Learning Applications for Self-Organized Wireless Networks
  • 45 Downloads

Abstract

For the data fusion of multi-sensors, the determination of weight directly affects the accuracy and performance of the fusion algorithm. In order to improve the accuracy of fusion algorithm, an adaptive weighted algorithm based on fuzzy preference relations is proposed. The degree of preference between signals is represented by introducing the improved logsig function, and then, the weight is calculated by fuzzy preference relations. Simulation results show that the proposed algorithm is significantly better than the mean value method, and the accuracy is basically equivalent to the method based on correlation function. The analysis of the actual vibration signals in axis system verifies the validity of the algorithm in the practical application. The algorithm in this paper has good dynamic performance and is easy to be implemented. It can be applied to the actual multi-vibration signal estimation to provide more accurate parameters for the next step of fault diagnosis.

Keywords

Fuzzy preference relations Weight Data fusion Multi-sensors signals Adaptive weighted estimate algorithm 

Notes

Acknowledgements

This work was supported by the National key R & D plan (Grant: 2017YFD0710201 and 2016YFD0702103), Shandong Province Natural Science Foundation of China (Grant: 2017GGX30105, 2018CXGC0601 and 2017CXGC0903), Innovation plan of agricultural machinery and equipment in Shandong province (Grant: 2018YZ002 and 2017YF006-02).

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Huijuan Hao
    • 1
  • Maoli Wang
    • 1
  • Yongwei Tang
    • 1
    Email author
  • Qingdang Li
    • 2
    • 3
  1. 1.Qilu University of Technology (Shandong Academy of Sciences), Shandong Computer Science Center (National Supercomputer Center in Jinan)Shandong Provincial Key Laboratory of Computer NetworksJinanChina
  2. 2.Technological Electronics Department, Faculty of Electrical Engineering and Computer Science, Institute of Nanostructure Technologies and AnalyticsUniversity of KasselKasselGermany
  3. 3.Chinesisch-Deutsche Technische FakultatQingdao University of Science and TechnologyQingdaoChina

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