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


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.


Health status Prediction Motor Multi-sensor Multi-feature parameters 



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.


  1. 1.
    Deng, L., Zhao, R.: A vibration analysis method based on hybrid techniques and its application to rotating machinery. Measurement 46(9), 3671–3682 (2013). CrossRefGoogle Scholar
  2. 2.
    Xiao, Y., Ding, E., Chen, C., Liu, X., Li, L.: A novel characteristic frequency bands extraction method for automatic bearing fault diagnosis based on Hilbert–Huang transform. Sensors 15(11), 27869–27893 (2015). CrossRefGoogle Scholar
  3. 3.
    Chen, B.Y., Li, H.R., Yu, H., Wang, Y.K.: A hybrid domain degradation feature extraction method for motor bearing based on distance evaluation technique. Int. J. Rotating Mach. 22(2017), 1–11 (2017). CrossRefGoogle Scholar
  4. 4.
    Jiang, W., Xie, C., Zhuang, M., Shou, Y., Tang, Y.: Sensor data fusion with Z-numbers and its application in fault diagnosis. Sensors 16(9), 1509 (2016). CrossRefGoogle Scholar
  5. 5.
    Yin, Y., Liu, F., Zhou, X., Li, Q.Z.: An efficient data compression model based on spatial clustering and principal component analysis in wireless sensor networks. Sensors 15(8), 19443–19465 (2015). CrossRefGoogle Scholar
  6. 6.
    Widodo, A., Yang, B.S.: Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst. Appl. 33(1), 241–250 (2007). CrossRefGoogle Scholar
  7. 7.
    Zhou, H.T., Chen, J., Dong, G.M., Wang, H.C., Yuan, H.D.: Bearing fault recognition method based on neighbourhood component analysis and coupled hidden markov model. Mech. Syst. Signal Process. 66–67, 568–581 (2016). CrossRefGoogle Scholar
  8. 8.
    Djeziri, M.A., Benmoussa, S., Sanchez, R.: Hybrid method for remaining useful life prediction in wind turbine systems. Renew. Energy 116, 173–187 (2018). CrossRefGoogle Scholar
  9. 9.
    Wu, Z.R., Li, X., Fang, L., et al.: Multiaxial fatigue life prediction based on nonlinear continuum damage mechanics and critical plane method. J. Mater. Eng. Perform. 27(6), 3144–3152 (2018). CrossRefGoogle Scholar
  10. 10.
    Wang, Z.Q., Hu, C.H., Fan, H.D.: Real-time remaining useful life prediction for a nonlinear degrading system in service: application to bearing data. IEEE/ASME Trans. Mechatron. 23(1), 211–222 (2018). CrossRefGoogle Scholar
  11. 11.
    Lin, J., Su, L., Yan, Y., et al.: Prediction method for power transformer running state based on LSTM_DBN network. Energies 11(7), 1880 (2018). CrossRefGoogle Scholar
  12. 12.
    Wang, Y.S., Ma, Q.H., Zhu, Q., Liu, X.T., Zhao, L.H.: An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine. Appl. Acoust. 75(1), 1–9 (2014). CrossRefGoogle Scholar
  13. 13.
    Climente-Alarcon, V., Antonino-Daviu, J.A., Strangas, E.G., Riera-Guasp, M.: Rotor-bar breakage mechanism and prognosis in an induction motor. IEEE Trans. Industr. Electron. 62(3), 1814–1825 (2013). CrossRefGoogle Scholar
  14. 14.
    Braccesi, C., Morettini, G., Cianetti, F., et al.: Development of a new simple energy method for life prediction in multiaxial fatigue. Int. J. Fatigue 112, 1–8 (2018). CrossRefGoogle Scholar
  15. 15.
    Rong, P.: A bayes approach to reliability prediction utilizing data from accelerated life tests and field failure observations. Qual. Reliab. Eng. Int. 25(2), 229–240 (2009). CrossRefGoogle Scholar
  16. 16.
    Otman, B., Yuan, X.H.: Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Inf. Fusion 8(4), 379–386 (2007). CrossRefGoogle Scholar
  17. 17.
    Wang, X., He, Y., Wang, L.: Study on mutual information and fractal dimension-based unsupervised feature parameters selection: application in UAVs. Entropy 20(9), 674 (2018). CrossRefGoogle Scholar
  18. 18.
    Michael, M., Lin, W.C.: Experimental study of information measure and inter-intra class distance ratios on feature selection and orderings. IEEE Trans. Syst. Man Cybern. 3(2), 172–181 (2010). CrossRefGoogle Scholar
  19. 19.
    Xue, X., Zhou, J.: A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery. ISA Trans. 66, 284–295 (2016). CrossRefGoogle Scholar
  20. 20.
    Deng, A.M., Chen, X., Zhang, C.H., Wang, Y.S.: Reliability assessment based on performance degradation data. J. Astronaut. 27(3), 546–552 (2006). CrossRefGoogle Scholar
  21. 21.
    Whitmore, G.A., Schenkelberg, F.: Modelling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Anal. 3(1), 27–45 (1997). CrossRefzbMATHGoogle Scholar
  22. 22.
    Bagdonavicius, V., Nikulin, M.S.: Estimation in degradation models with explanatory variables. Lifetime Data Anal. 7(1), 85–103 (2001). MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Liu, H.M., Lv, C., Ouyang, P.C., Wang, S.: Helicopter rotor tuning based on neural network and particle swarm optimization. J. Beijing Univ. Aeronaut. Astronaut. 37(3), 283–288 (2011). CrossRefGoogle Scholar
  24. 24.
    Jin, G., Matthews, D., Fan, Y., Liu, Q.: Physics of failure-based degradation modeling and lifetime prediction of the momentum wheel in a dynamic covariate environment. Eng. Fail. Anal. 28(3), 222–240 (2013). CrossRefGoogle Scholar
  25. 25.
    Luo, Y.: Safety Economics, 2nd edn. Chemical Industry Press, Beijing (2010)Google Scholar
  26. 26.
    Márquez, A.C.: The Maintenance Management Framework, 1st edn. Springer, London (2007)Google Scholar

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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