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Motor Health Status Prediction Method Based on Information from Multi-sensor and Multi-feature Parameters

  • Lizhi Wang
  • Yusheng Sun
  • Yidi He
  • Xuejiao Zhao
  • Wenhui Fan
  • Xiaohong WangEmail author
Article
  • 70 Downloads

Abstract

Health status prediction is of great significance for a motor system’s safe operation and lifecycle management. The object of this work is to achieve better information fusion performance for information obtained from X-, Y-, and Z-axial and existed in the multi-feature parameter, and therefore gain more comprehensively and effectively prediction results of health status. First, a UAV power motor is chosen as the test item to obtain the original vibration data. Then, the multi-feature parameters are fused and chosen based on quality and quantity method considering the diagnosis results and degradation path descriptive ability. Next, the health status prediction is achieved with Bayesian updating algorithm. Finally, a DS theory and information entropy weight-based granulation fusion method of multi-source health status information for the electric motor is proposed. The method can achieve the fusion of multiple prediction results obtained from multi-feature parameters to gain the optimal health status prediction result for the motor. The result is compared with actual data and also verified by information entropy. Meanwhile, according to the prediction results, its application in risk assessment and maintenance planning were discussed.

Keywords

Health status Prediction Motor Multi-sensor Multi-feature parameters 

Notes

Acknowledgements

This work is supported by Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences (Project No. XDA14000000) and by the Aero-Science Fund (Grant No. 2015ZD51044).

Compliance with Ethical Standards

Conflicts of interest

The authors declare no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Lizhi Wang
    • 1
    • 2
  • Yusheng Sun
    • 3
  • Yidi He
    • 3
  • Xuejiao Zhao
    • 3
  • Wenhui Fan
    • 3
  • Xiaohong Wang
    • 3
    Email author
  1. 1.Institute of Unmanned SystemBeihang UniversityBeijingChina
  2. 2.Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System of Ministry of Industry and Information TechnologyBeijingChina
  3. 3.School of Reliability and Systems EngineeringBeihang UniversityBeijingChina

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