Abstract
Bearing and planetary gearbox are important for rotating machinery. However, their faults often cause the stop of the machinery or even fatal casualties. Vibration signal contains the status information of the rotating machinery, which is covered by the strong noise. Stochastic resonance (SR) is a noise-benefit phenomenon, which can detect the weak fault characteristic signal from the vibration signal under strong noise. To detect the fault of bearing or planetary gearbox effectively, SR based on Wood–Saxon potential which only has on potential well called WSSR is studied, and a novel fault diagnosis strategy based on WSSR is proposed. The effect of every WSSR parameter, anti-noise capability of WSSR under different noise intensities and optimal frequency response of WSSR under different driving frequency are analyzed by simulation. To verify the effectiveness of our proposed fault diagnosis strategy based on WSSR, three preset fault tests of bearing and two of planetary gearbox are carried out. Bi-stable SR is also used for comparison. The results show that our proposed fault diagnosis strategy is more effective for the fault detection of bearing and planetary gearbox than bi-stable SR.
Similar content being viewed by others
References
Xiao S, Liu S, Jiang F, Song M, Cheng S (2019) Nonlinear dynamic response of reciprocating compressor system with rub-impact fault caused by subsidence. J Vib Control 25(11):1737–1751. https://doi.org/10.1177/1077546319835281
Sharma A, Amarnath M, Kankar PK (2017) Novel ensemble techniques for classification of rolling element bearing faults. J Braz Soc Mech Sci Eng 39(3):709–724. https://doi.org/10.1007/s40430-016-0540-8
Smith WA, Fan Z, Peng Z, Li H, Randall RB (2016) Optimised spectral kurtosis for bearing diagnostics under electromagnetic interference. Mech Syst Sig Process 75:371–394. https://doi.org/10.1016/j.ymssp.2015.12.034
Mishra C, Samantaray AK, Chakraborty G (2017) Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising. Meas 103:77–86. https://doi.org/10.1016/j.measurement.2017.02.033
Yoon J, He D, Hecke BV (2015) On the use of a single piezoelectric strain sensor for wind turbine planetary gearbox fault diagnosis. IEEE Trans Ind Electron 62(10):6585–6593. https://doi.org/10.1109/TIE.2015.2442216
Feng Z, Liang M (2014) Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis. Renew Energy 66:468–477. https://doi.org/10.1016/j.renene.2013.12.047
Feng Z, Chen X, Liang M (2015) Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. Mech Syst Sig Process 52–53:360–375. https://doi.org/10.1016/j.ymssp.2014.07.009
Gao J, Wang R, Zhang R, Li Y (2016) A novel fault diagnosis method for rotating machinery based on S transform and morphological pattern spectrum. J Braz Soc Mech Sci Eng 38(6):1575–1584. https://doi.org/10.1007/s40430-015-0474-6
Yan X, Jia M (2019) Improved singular spectrum decomposition-based 1.5-dimensional energy spectrum for rotating machinery fault diagnosis. J Braz Soc Mech Sci Eng 41(1):50. https://doi.org/10.1007/s40430-018-1503-z
Benzi R, Sutera A, Vulpiani A (1981) The mechanism of stochastic resonance. J Phys A Math Gen 14:L453–L457. https://doi.org/10.1088/0305-4470/14/11/006
Yang Y, Jiang Z, Xu B, Repperger DW (2009) An investigation of two-dimensional parameter-induced stochastic resonance and applications in nonlinear image processing. J Phys A Math Gen https://doi.org/10.1088/1751-8113/42/14/145207
Mcinnes CR, Gorman D, Cartmell MP (2008) Enhanced vibrational energy harvesting using non-linear stochastic resonance. J Sound Vib 318(4):655–662. https://doi.org/10.1016/j.jsv.2008.07.017
Duan F, Xu B (2003) Parameter-induced stochastic resonance and baseband binary pam signals transmission over an awgn channel. Int J Bifurc Chaos 13(2):411–425. https://doi.org/10.1142/S0218127403006601
Li Q, Wang T, Leng Y, Wang W, Wang G (2007) Engineering signal processing based on adaptive step-changed stochastic resonance. Mech Syst Sig Process 21(5):2267–2279. https://doi.org/10.1016/j.ymssp.2006.10.003
Tan J, Chen X, Wang J, Chen H, Cao H, Zi Y, He Z (2009) Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis. Mech Syst Sig Process 23:811–822. https://doi.org/10.1016/j.ymssp.2008.07.011
Zhang X, Hu N, Cheng Z, Hu L (2012) Enhanced detection of rolling element bearing fault based on stochastic resonance. Chin J Mech Eng 25(6):1287–1297. https://doi.org/10.3901/CJME.2012.06.1287
Lu S, He Q, Wang J (2019) A review of stochastic resonance in rotating machine fault detection. Mech Syst Sig Process 116:230–260. https://doi.org/10.1016/j.ymssp.2018.06.032
Li J, Li M, Zhang J, Jiang G (2019) Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind turbine. Meas 133:421–432. https://doi.org/10.1016/j.measurement.2018.10.054
Zhang X, Wang J, Liu Z, Wang J (2018) Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance. ISA Trans https://doi.org/10.1016/j.isatra.2018.09.022
He Q, Wu E, Pan Y (2018) Multi-scale stochastic resonance spectrogram for fault diagnosis of rolling element bearings. J Sound Vib 420:174–184. https://doi.org/10.1016/j.jsv.2018.01.001
Mba CU, Makis V, Marchesiello S, Fasana A, Garibaldi L (2018) Condition monitoring and state classification of gearboxes using stochastic resonance and hidden Markov models. Meas 126:76–95. https://doi.org/10.1016/j.measurement.2018.05.038
Lu S, He Q, Zhang H, Kong F (2017) Rotating machine fault diagnosis through enhanced stochastic resonance by full-wave signal construction. Mech Syst Sig Process 85:82–97. https://doi.org/10.1016/j.ymssp.2016.08.003
Gammaitoni L, Hänggi P, Jung P, Marchesoni F (1998) Stochastic resonance. Rev Mod Phys 70(1):223–287. https://doi.org/10.1103/RevModPhys.70.223
Woods RD, Saxon DS (1954) Diffuse surface optical model for nucleon–nuclei scattering. Phys Rev 95(2):577–578. https://doi.org/10.1103/PhysRev.95.577
Chi K, Kang J, Zhang X, Yang Z (2018) Bearing fault diagnosis based on stochastic resonance with cuckoo search. Int J Perform Eng 14(3):413–424. https://doi.org/10.23940/ijpe.18.03.p2.413424
Chi K, Kang J, Zhao F, Liu L (2019) An adaptive underdamped stochastic resonance based on NN and CS for bearing fault diagnosis. Int J Syst Assur Eng Manag 10(3):437–452. https://doi.org/10.1007/s13198-019-00816-7
Author information
Authors and Affiliations
Corresponding author
Additional information
Technical Editor: José Roberto de França Arruda.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chi, K., Kang, J., Zhang, X. et al. Experimental application of stochastic resonance based on Wood–Saxon potential on fault diagnosis of bearing and planetary gearbox. J Braz. Soc. Mech. Sci. Eng. 41, 514 (2019). https://doi.org/10.1007/s40430-019-1999-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40430-019-1999-x