Abstract
Toward ever improvement technology, a diagnostic procedure making use of Dual Q-Factor wavelet decomposition (DQWD) and adaptive wavelet transform (AWT) is proposed for the extraction of weak bearing defect feature. The vibration signal of bearing consists of mix of transient impulse (low-Q factor) and oscillatory signal (high-Q factor signal). Therefore, to separate the two different behavioral signals, Dual Q-factor wavelet decomposition is carried out. The DQWD decompose any signal into low-Q factor and high-Q factor signal. Further, extraction of feature is carried out by adaptive wavelet transform. For this adaptive wavelet is extracted from the low-Q factor signal using least square fitting method. The generated wavelet is applied to low-Q factor signal to produce AWT scalogram. Then, coefficients of resulting scalogram are integrated with respect to scale for each time segment. Then, envelope demodulation is applied to the resulting waveform to spot the defect frequency. An experimental study is presented to show the effectiveness of the proposed method. The proposed method is also effective over EMD and EEMD technique in isolating the transient impulse of defect from the oscillatory part of the signal.
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References
Cai G, Chen X, He Z (2013) Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox. Mech Syst Signal Process 41:34–53. https://doi.org/10.1016/j.ymssp.2013.06.035
Hong H, Liang M (2009) Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform. J Sound Vib 320(1–2):452–468. https://doi.org/10.1016/j.jsv.2008.07.011
Kumar A, Kumar R (2017a) Least square fitting for adaptive wavelet generation and automatic prediction of defect size in the bearing using Levenberg–Marquardt Backpropagation. J Nondestruct Eval 36(1). https://doi.org/10.1007/s10921-016-0385-1
Kumar A, Kumar R (2017b). Adaptive artificial intelligence for automatic identification of defect in the angular contact bearing. Neural Comput Appl 1–11. https://doi.org/10.1007/s00521-017-3123-4
Kumar A Kumar R (2017c) Oscillatory behaviour based wavelet decomposition for bearing condition monitoring in the centrifugal pumps. Proc IMechE, Part J: J Eng Tribol. (in press). http://doi.org/10.1177/1350650117727976
Liang B, Iwnicki SD, Zhao Y (2013) Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis. Mech Syst Signal Process 39(1–2):342–360. https://doi.org/10.1016/j.ymssp.2013.02.016
McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review. Tribol Int 17(1):3–10. https://doi.org/10.1016/0301-679X(84)90076-8
Ming AB, Zhang W, Qin ZY, Chu FL (2015) Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis. Mech Syst Signal Process 50–51:70–100. https://doi.org/10.1016/j.ymssp.2014.05.033
Randall RB, Antoni J (2011) Rolling element bearing diagnostics-a tutorial. Mech Syst Signal Process 25(2):485–520. https://doi.org/10.1016/j.ymssp.2010.07.017
Selesnick IW (2011) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59(8):3560–3575. https://doi.org/10.1109/TSP.2011.2143711
Singh M, Yadav RK, Kumar R (2013) Discrete wavelet transform based measurement of inner race defect width in taper roller bearing. Mapan-J Metrol Soc I 28(1):17–23. https://doi.org/10.1007/s12647-013-0045-1
Wang H, Chen J, Dong G (2014) Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. J Sound Vib 48(1–2):103–119. https://doi.org/10.1016/j.ymssp.2014.04.006
Wang Y, Xu G, Liang L, Jiang K (2015) Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mech Syst Signal Process 54:259–276. https://doi.org/10.1016/j.ymssp.2014.09.002
Zhang Y, Bingham C, Yang Z, Ling BW-K, Gallimore M (2014) Machine fault detection by signal denoising—with application to industrial gas turbines. Measurement 58:230–240. https://doi.org/10.1016/j.measurement.2014.08.020
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Kumar, A., Prakash, R., Kumar, R. (2019). Separation of Impulse from Oscillation for Detection of Bearing Defect in the Vibration Signal. In: Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2018. Applied Condition Monitoring, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-11220-2_29
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DOI: https://doi.org/10.1007/978-3-030-11220-2_29
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