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A Detecting System for Wheel Balancer Based on the Effect Coefficient Method

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 885))

Abstract

According to the characteristics of the wheel balancer, it analyzed the balance principle of the effect coefficient method, and displayed the overall design scheme and software flow of the system. Through different specifications of the wheel detection, the balance effect is satisfactory.

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References

  1. Zhang, H.H., Bai, Y.: A smart diagnosis system based on automatic recognition of multiple rotor faults. Adv. Mech. Eng. 9(9), 1–12 (2017)

    Google Scholar 

  2. Li, C., et al.: Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram. Mech. Syst. Signal Process. 76–77, 157–173 (2016)

    Article  Google Scholar 

  3. Zarei, J., Tajeddini, M.A., Karimi, H.R.: Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24(2), 151–157 (2014)

    Article  Google Scholar 

  4. Zhang, S.H., Li, W.H.: Bearing condition recognition and degradation assessment under varying running conditions using NPE and SOM. Math. Probl. Eng. 1, 1–10 (2014)

    Google Scholar 

  5. Zhang, H.H.: Research on knowledge based rotor fault diagnosis theory and method. Doctoral dissertation, Tsinghua University, Beijing: June 1993

    Google Scholar 

  6. Tang, X.K.: Mechanical Dynamics. Higher Education Press, Beijing (1986)

    Google Scholar 

  7. Li, C., Sanchez, R.V., Zurita, G., et al.: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168, 119–127 (2015)

    Article  Google Scholar 

  8. Chen, J.L.: Measures to improve the dynamic balancing performance of tires. Automobile Appl. Technol. 10 (2017)

    Google Scholar 

  9. Li, C., Liang, M., Wang, T.Y.: Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals. Mech. Syst. Signal Process. 64–65, 132–148 (2015)

    Article  Google Scholar 

  10. Jannati, M., Sutikno, T., Idris, N.R.N., et al.: High performance speed control of single-phase induction motors using switching forward and backward EKF strategy. Int. J. Power Electron. Drive Syst 7(1), 17–27 (2016)

    Google Scholar 

  11. Li, C., Cerrada, M., Cabrera, D., et al.: A comparison of fuzzy clustering algorithms for bearing fault diagnosis. J. Intell. Fuzzy Syst. 34(6), 3565–3580 (2018)

    Article  Google Scholar 

  12. Bai, Y., Sun, Z.Z., Zeng, B., et al.: A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. J. Intell. Manuf. (2018). https://doi.org/10.1007/s10845-017-1388-1

    Article  Google Scholar 

  13. Long, J.Y., Sun, Z.Z., Chen, H.B., et al.: Variable neighborhood search for integrated determination of charge batching and casting start time in steel plants. J. Intell. Fuzzy Syst. 34(6), 3821–3832 (2018)

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (51775112), the Research Program of Higher Education of Guangdong (2016KZDXM054), and the DGUT Research Project (GC300501-08, KCYKYQD2017011).

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Correspondence to Honghui Zhang .

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Zhang, H., Zhang, W. (2019). A Detecting System for Wheel Balancer Based on the Effect Coefficient Method. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent, Interactive Systems and Applications. IISA 2018. Advances in Intelligent Systems and Computing, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-02804-6_9

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