Abstract
The interference from background noise makes it difficult to identify the incipient bearing defect via vibration analysis. Stochastic resonance (SR) is a nonlinear phenomenon which is characterized by that the output signal can be enhanced with the assistance of the proper noise. The Large Parameter Bistable SR (LPBSR) method is commonly used in the bearing fault diagnosis. However, the LPBSR method requires signal tuning to satisfy the small parameter requirement (both amplitude and frequency are far less than 1), which implies the inherent structure of the input signal is modified and the effect of SR may be further affected. Additionally, a barrier exists in the bistable model, which indicates that the particle motion in the bistable potential is unstable and then the external noises induced by the unstable motion are introduced in the output signal. This chapter proposes a new strategy to realize bearing defect diagnosis, that is, a Woods-Saxon potential instead of a conventional bistable potential is utilized to achieve SR and to enhance the output signal-to-noise ratio (SNR). In the proposed method, the output SNR can be optimized just by tuning the parameters of the Woods-Saxon potential. This method overcomes the limitation of the small parameter requirement of the classical bistable SR, and can thus detect a high driving frequency. Furthermore, the smooth Woods-Saxon potential leads to a more stable particle motion compared to the bistable potential, which provides a more regular output waveform and reduces the unexpected noises at the same time. The proposed method has yielded more effective results than the traditional methods, which was verified by means of a practical bearing vibration signal carrying defect information.
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References
Wang CT, Gao RX (2003) Wavelet transform with spectral post-processing for enhanced feature extraction. IEEE Trans Instrum Meas 52(4):1296–1301
Yaqub MF, Gondal I, Kamruzzaman J (2012) Inchoate Fault detection framework: adaptive selection of wavelet nodes and cumulant orders. IEEE Trans Instrum Meas 61(3):685–695
Neville S, Dimopoulos N (2006) Wavelet denoising of coarsely quantized signals. IEEE Trans Instrum Meas 55(3):892–901
Ho D, Randall RB (2000) Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech Syst Signal Pr 14(5):763–788
Gammaitoni L, Hanggi P, Jung P, Marchesoni F (1998) Stochastic resonance. Rev Mod Phys 70(1):223–287
Nishiguchi K, Fujiwara A (2012) Detecting signals buried in noise via nanowire transistors using stochastic resonance. Appl Phys Lett 101(19):193108
Dai D, He Q (2012) Multiscale noise tuning stochastic resonance enhances weak signal detection in a circuitry system. Meas Sci Technol 23(11):115001
Xu B, Zhang H, Zeng L, Li J, Wu X, Jiang Z-P (2007) Application of parameter-induced stochastic resonance to target detection in shallow-water reverberation. Appl Phys Lett 91(9):091908
Leng YG, Leng YS, Tai YW, Yan G (2006) Numerical analysis and engineering application of large parameter stochastic resonance. J Sound Vibrat 292(3–5):788–801
Yang DX, Hu NQ (2004) Detection of weak aperiodic shock, signal based on stochastic resonance. In: 3rd International Symposium on Instrumentation Science and Technology, Xi’an, China, 0210–0213
Lu S, He Q, Zhang H, Zhang S, Kong F (2013) Note: Signal amplification and filtering with a tristable stochastic resonance cantilever. Rev Sci Instrum 84(2):026110
Bohr A, Mottelson BR (1975) Nuclear structure, vol 1. Benjamin, New York
Deza JI, Deza RR, Wio HS (2012) Wide-spectrum energy harvesting out of colored Levy-like fluctuations, by monostable piezoelectric transducers. Europhys Lett 100(3):38001
Shen C, He Q, Kong F, Peter WT (2013) A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis. J Mech Eng Sci 227(6):1362–1370
http://csegroups.case.edu/bearingdatacenter/pages/download-data-file
Peng ZK, Peter WT, Chu FL (2005) An improved Hilbert-Huang transform and its application in vibration signal analysis. J Sound Vibrat 286(1–2):187–205
Acknowledgment
This work was supported by the National Natural Science Foundation of China (51075379, 51005221 and 11274300). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.
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Lu, S., He, Q., Kong, F. (2015). Bearing Defect Diagnosis by Stochastic Resonance Based on Woods-Saxon Potential. In: Tse, P., Mathew, J., Wong, K., Lam, R., Ko, C. (eds) Engineering Asset Management - Systems, Professional Practices and Certification. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-09507-3_10
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DOI: https://doi.org/10.1007/978-3-319-09507-3_10
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