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Unknown bearing fault diagnosis under time-varying speed conditions and strong noise background

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Abstract

The bearing vibration signal shows strong non-stationary property under time-varying speed conditions. In addition, the weak bearing fault characteristic is often submerged in strong background noise. How to accurately extract the unknown fault characteristic from the non-stationary vibration signal is the primary problem of bearing fault diagnosis. Stochastic resonance has been proved to be an effective weak signal enhancement method. Therefore, an unknown bearing fault detection technology of speed variation is proposed, which breaks through the periodicity limitation of the classical stochastic resonance on the input signal. It enables stochastic resonance suitable for the enhancement of non-stationary fault signal. Firstly, the non-stationary vibration signal is processed by the computed order tracking to obtain the stationary signal in angular domain. To extract the potential feature information, the bearing imaginary fault order index is constructed from the angular domain order spectrum. Then, the resonance response at the imaginary fault order is obtained. Finally, the coherence resonance theory is introduced to judge the bearing fault pattern through the resonance factor index of response order spectrum. The proposed method overcomes the fuzzy mapping relationship between the signal symptom and the bearing fault caused by speed variation. The experimental data analysis results provide effective support for the proposed method.

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Data availability

The experimental data can be provided by the corresponding author on reasonable request.

References

  1. Al-Regib, E., Ni, J., Lee, S.H.: Programming spindle speed variation for machine tool chatter suppression. Int. J. Mach. Tools Manuf 43(12), 1229–1240 (2003)

    Article  Google Scholar 

  2. Villa, L.F., Reñones, A., Perán, J.R., De Miguel, L.J.: Angular re-sampling for vibration analysis in wind turbines under non-linear speed fluctuation. Mech. Syst. Signal Process. 25(6), 2157–2168 (2011)

    Article  Google Scholar 

  3. Sharma, A., Amarnath, M., Kankar, P.K.: Nonlinear dynamic analysis of defective rolling element bearing using Higuchi’s fractal dimension. Sādhanā. 44(4), 1–29 (2019)

    Article  MathSciNet  Google Scholar 

  4. Sharma, A., Upadhyay, N., Kankar, P.K., Amarnath, M.: Nonlinear dynamic investigations on rolling element bearings: A review. Adv. Mech. Eng. 10(3), 1–15 (2018)

    Article  Google Scholar 

  5. Sharma, A., Amarnath, M., Kankar, P. K.: Effect of unbalanced rotor on the dynamics of cylindrical roller bearings. In: Proceedings of the 9th IFToMM International Conference on Rotor Dynamics Mechanisms and Machine Science. 21, 1653–1663 (2015)

  6. Hasan, M.J., Islam, M.M., Kim, J.M.: Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions. Measurement 138, 620–631 (2019)

    Article  Google Scholar 

  7. Liu, J.: Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection. Meas. Sci. Technol. 23, 1–11 (2012)

    Article  Google Scholar 

  8. Randall, R.B., Antoni, J.: Rolling element bearing diagnostics–A tutorial. Mech. Syst. Signal Process. 25(2), 485–520 (2011)

    Article  Google Scholar 

  9. Brandt, A., Lago, T., Ahlin, K., Tuma, J.: Main principles and limitations of current order tracking methods. Sound Vib. 39(3), 19–22 (2005)

    Google Scholar 

  10. Potter, R., Gribler, M.: Computed order tracking obsoletes older methods. SAE Int. 61-67 (1989)

  11. Bonnardot, F., El Badaoui, M., Randall, R.B., Daniere, J., Guillet, F.: Use of the acceleration signal of a gearbox in order to perform angular re-sampling (with limited speed fluctuation). Mech. Syst. Signal Process. 19(4), 766–785 (2005)

    Article  Google Scholar 

  12. Wang, T., Liang, M., Li, J., Cheng, W.: Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis. Mech. Syst. Signal Process. 45(1), 139–153 (2014)

    Article  Google Scholar 

  13. Wang, Y., Peter, W.T., Tang, B., Qin, Y., Deng, L., Huang, T., Xu, G.: Order spectrogram visualization for rolling bearing fault detection under speed variation conditions. Mech. Syst. Signal Process. 122, 580–596 (2019)

    Article  Google Scholar 

  14. Combet, F., Gelman, L.: An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor. Mech. Syst. Signal Process. 21(6), 2590–2606 (2007)

    Article  Google Scholar 

  15. Wang, Y., Xu, G., Zhang, Q., Liu, D., Jiang, K.: Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions. J. Sound Vib. 348, 381–396 (2015)

    Article  Google Scholar 

  16. Fyfe, K.R., Munck, E.D.S.: Analysis of computed order tracking. Mech. Syst. Signal Process. 11(2), 187–205 (1997)

    Article  Google Scholar 

  17. Bossley, K.M., Mckendrick, R.J., Harris, C.J., Mercer, C.: Hybrid computed order tracking. Mech. Syst. Signal Process. 13(4), 627–641 (1999)

    Article  Google Scholar 

  18. Wang, Y., Yang, L., Xiang, J., Yang, J., He, S.: A hybrid approach to fault diagnosis of roller bearings under variable speed conditions. Meas. Sci. Technol. 28(12), 125104 (2017)

    Article  Google Scholar 

  19. Li, H., Zhang, Y., Zheng, H.: Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform. J. Mech. Sci. Technol. 24(3), 811–822 (2010)

    Article  MathSciNet  Google Scholar 

  20. Borghesani, P., Ricci, R., Chatterton, S., Pennacchi, P.: A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions. Mech. Syst. Signal Process. 38(1), 23–35 (2013)

    Article  Google Scholar 

  21. Wang, X., Zhou, F., He, Y., Wu, Y.: Weak fault diagnosis of rolling bearing under variable speed condition using IEWT-based enhanced envelope order spectrum. Meas. Sci. Technol. 30(3), 035003 (2019)

    Article  Google Scholar 

  22. Mishra, C., Samantaray, A.K., Chakraborty, G.: Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate. Mech. Syst. Signal Process. 72, 206–222 (2016)

    Article  Google Scholar 

  23. Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.: Stochastic resonance. Rev. Mod. Phys. 70(1), 223 (1998)

    Article  Google Scholar 

  24. Benzi, R., Sutera, A., Vulpiani, A.: The mechanism of stochastic resonance. J. Phys. A: Math. Theor. 14(11), L453 (1981)

    Article  MathSciNet  Google Scholar 

  25. Lu, S., He, Q., Wang, J.: A review of stochastic resonance in rotating machine fault detection. Mech. Syst. Signal Process. 116, 230–260 (2019)

    Article  Google Scholar 

  26. Qiao, Z., Lei, Y., Lin, J., Jia, F.: An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis. Mech. Syst. Signal Process. 84, 731–746 (2017)

    Article  Google Scholar 

  27. López, C., Zhong, W., Lu, S., Cong, F., Cortese, I.: Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection. J. Sound Vib. 411, 34–46 (2017)

    Article  Google Scholar 

  28. Sharma, A., Amarnath, M., Kankar, P.K.: Feature extraction and fault severity classification in ball bearings. J. Vib. Control 22(1), 176–192 (2016)

    Article  Google Scholar 

  29. Sharma, A., Amarnath, M., Kankar, P.K.: Novel ensemble techniques for classification of rolling element bearing faults. J. Braz. Soc. Mech. Sci. 39(3), 709–724 (2017)

    Article  Google Scholar 

  30. He, Q., Wu, E., Pan, Y.: Multi-scale stochastic resonance spectrogram for fault diagnosis of rolling element bearings. J. Sound Vib. 420, 174–184 (2018)

    Article  Google Scholar 

  31. Zhang, X., Kang, J., Hao, L., Cai, L., Zhao, J.: Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution. J. Vibroeng. 17(1), 243–260 (2015)

    Google Scholar 

  32. Li, J., Chen, X., He, Z.: Adaptive stochastic resonance method for impact signal detection based on sliding window. Mech. Syst. Signal Process. 36(2), 240–255 (2013)

    Article  Google Scholar 

  33. Huang, D., Yang, J., Zhou, D., Sanjuán, M.A., Liu, H.: Recovering an unknown signal completely submerged in strong noise by a new stochastic resonance method. Commun. Nonlinear Sci. 66, 156–166 (2019)

    Article  MATH  Google Scholar 

  34. Li, J., Zhang, Y., Xie, P.: A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis. Measurement 91, 499–508 (2016)

    Article  Google Scholar 

  35. Lin, Y., Ye, C.: Adaptive stochastic resonance quantified by a novel evaluation index for rotating machinery fault diagnosis. Measurement 184, 109920 (2021)

    Article  Google Scholar 

  36. Wang, J., He, Q., Kong, F.: Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings. IEEE Trans. Instrum. Meas. 64(2), 564–577 (2014)

    Article  Google Scholar 

  37. Yang, C., Yang, J., Zhu, Z., Shen, G., Zheng, Y.: Distinguish coherence resonance and stochastic resonance in bearing fault evaluation. Meas. Sci. Technol. 31(4), 045001 (2020)

    Article  Google Scholar 

  38. Gang, H., Ditzinger, T., Ning, C.Z., Haken, H.: Stochastic resonance without external periodic force. Phys. Rev. Lett. 71(6), 807–810 (1993)

    Article  Google Scholar 

  39. Pikovsky, A.S., Kurths, J.: Coherence resonance in a noise-driven excitable system. Phys. Rev. Lett. 78(5), 775 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  40. Lindner, B., Garcıa-Ojalvo, J., Neiman, A., Schimansky-Geier, L.: Effects of noise in excitable systems. Phys. Rep. 392(6), 321–424 (2004)

    Article  Google Scholar 

  41. Zhao, M., Lin, J., Wang, X., Lei, Y., Cao, J.: A tacho-less order tracking technique for large speed variations. Mech. Syst. Signal Process. 40(1), 76–90 (2013)

    Article  Google Scholar 

  42. Saavedra, P.N., Rodriguez, C.G.: Accurate assessment of computed order tracking. Shock. Vib. 13(1), 13–32 (2006)

    Article  Google Scholar 

  43. Fauve, S., Heslot, F.: Stochastic resonance in a bistable system. Phys. Lett. A 97(1–2), 5–7 (1983)

    Article  Google Scholar 

  44. Jung, P., Hänggi, P.: Amplification of small signals via stochastic resonance. Phys. Rev. A 44(12), 8032 (1991)

    Article  Google Scholar 

  45. Huang, D., Yang, J., Zhang, J., Liu, H.: An improved adaptive stochastic resonance method for improving the efficiency of bearing faults diagnosis. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 232(13), 2352–2368 (2018)

    Article  Google Scholar 

  46. Lu, J., Huang, M., Yang, J.J.: A novel spectrum sensing method based on tri-stable stochastic resonance and quantum particle swarm optimization. Wireless Pers. Commun. 95(3), 2635–2647 (2017)

    Article  Google Scholar 

  47. Zhang, X., Hu, N., Hu, L., Cheng, Z.: Multi-scale bistable stochastic resonance array: a novel weak signal detection method and application in machine fault diagnosis. Sci. China Technol. Sci. 56(9), 2115–2123 (2013)

    Article  Google Scholar 

  48. Feng, Z., Chen, X., Wang, T.: Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions. J. Sound Vib. 400, 71–85 (2017)

    Article  Google Scholar 

  49. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  50. Isham, M.F., Leong, M.S., Lim, M.H., Ahmad, Z.A.: Variational mode decomposition: mode determination method for rotating machinery diagnosis. J. Vibroeng. 20(7), 2604–2621 (2018)

    Article  Google Scholar 

  51. Zhang, M., Jiang, Z., Feng, K.: Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump. Mech. Syst. Signal Process. 93, 460–493 (2017)

    Article  Google Scholar 

  52. Li, Z., Chen, J., Zi, Y., Pan, J.: Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high-speed locomotive. Mech. Syst. Signal Process. 85, 512–529 (2017)

    Article  Google Scholar 

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Acknowledgements

The project was supported by the National Natural Science Foundation of China (Grant No. 12072362) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, People’s Republic of China

    Jianhua Yang, Chen Yang, Xuzhu Zhuang, Houguang Liu & Zhile Wang

  2. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Chen Yang

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  1. Jianhua Yang
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  2. Chen Yang
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Correspondence to Chen Yang.

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Yang, J., Yang, C., Zhuang, X. et al. Unknown bearing fault diagnosis under time-varying speed conditions and strong noise background. Nonlinear Dyn 107, 2177–2193 (2022). https://doi.org/10.1007/s11071-021-07078-8

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