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A Wavelet Spectrum Technique for Machinery Fault Diagnosis

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DOI: 10.4236/jsip.2011.24046    5,744 Downloads   9,675 Views   Citations

ABSTRACT

Rotary machines are widely used in various applications. A reliable machinery fault detection technique is critically needed in industries to prevent the machinery system’s performance degradation, malfunction, or even catastrophic failures. The challenge for reliable fault diagnosis is related to the analysis of non-stationary features. In this paper, a wavelet spectrum (WS) technique is proposed to tackle the challenge of feature extraction from these non-stationary signatures; this work will focus on fault detection in rolling element bearings. The vibration signatures are first analyzed by a wavelet transform to demodulate representative features; the periodic features are then enhanced by cross-correlating the resulting wavelet coefficient functions over several contributive neighboring wavelet bands. The effectiveness of the proposed technique is examined by experimental tests corresponding to different bearing conditions. Test results show that the developed WS technique is an effective signal processing approach for non-stationary feature extraction and analysis, and it can be applied effectively for bearing fault detection.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

D. Kanneg and W. Wang, "A Wavelet Spectrum Technique for Machinery Fault Diagnosis," Journal of Signal and Information Processing, Vol. 2 No. 4, 2011, pp. 322-329. doi: 10.4236/jsip.2011.24046.

References

[1] M. Patil, J. Mathew and P. Rajendrakumar, “Bearing Signature Analysis as a Medium for Fault Detection: A Review,” Journal of Tribology, Vol. 130, No. 1, 2008, pp. 1-7. doi:10.1115/1.2805445
[2] J. Liu, W. Wang and F. Golnaraghi, “An Enhanced Diagnostic Scheme for Bearing Fault Detection,” IEEE Transactions on Instrumentation and Measurement, Vol. 59, No.2, 2010, pp. 309-321. doi:10.1109/TIM.2009.2023814
[3] W. Wang, F. Golnaraghi and F. Ismail, “Condition Monitoring of a Multistage Printing Press,” Journal of Sound and Vibration, Vol. 270, No. 4-5, 2004, pp. 755-766. doi:10.1016/S0022-460X(03)00209-8
[4] J. R. Stack, T. G. Habetler and R. G. Harley, “Fault-Signature Modeling and Detection of Inner-Race Bearing Faults,” IEEE Transactions on Industry Applications, Vol. 42, No. 1, 2006, pp. 61-68. doi:10.1109/TIA.2005.861365
[5] B. Holm-Hansen, R. Gao and L. Zhang, “Customized Wavelet for Bearing Defect Detection,” Journal of Dynamic Systems, Measurement, and Control, Vol. 126, No. 4, 2004, pp. 740-745. doi:10.1115/1.1850534
[6] D. Shi, W. Wang and L. Qu, “Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform,” Journal of Vibration and Acoustics, Vol. 126, No. 4, 2004, pp. 567-573. doi:10.1115/1.1804995
[7] A. Jardine, D. Lin and D. Banjevic, “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance,” Mechanical Systems and Signal Processing, Vol. 20, No. 7, 2006, pp. 1483-1510. doi:10.1016/j.ymssp.2005.09.012
[8] Y. Choi and Y. Kim, “Fault Detection in a Ball Bearing System Using Minimum Variance Cepstrum,” Measurement Science and Technology, Vol. 18, No. 5, 2007, pp. 1433-1440. doi:10.1088/0957-0233/18/5/031
[9] P. Mcfadden and J. Smith, “Vibration Monitoring of Rolling Element Bearings by the High-Frequency Resonance Technique—A Review,” Tribology International, Vol. 17, No. 1, 1984, pp. 3-10. doi:10.1016/0301-679X(84)90076-8
[10] Y. Sheen, “An Analysis Method for the Vibration Signal with Amplitude Modulation in a Bearing System,” Journal of Sound and Vibration, Vol. 303, No. 3-5, 2007, pp. 538-552. doi:10.1016/j.jsv.2007.01.035
[11] T. Kaewkongka, Y. Au, R. Rakowski and B. Jones, “A Comparative Study of Short Time Fourier Transform and Continuous Wavelet Transform for Bearing Condition Monitoring,” International Journal of COMADEM, Vol. 6, 2003, pp. 41-48.
[12] B. Kim, S. Lee, M. Lee, J. Ni, J. Song and C. Lee, “A Comparative Study on Damage Detection in Speed-Up and Coast-Down Process of Grinding Spindle-Typed Rotor-Bearing System,” Journal of Materials Processing Technology, Vol. 187-188, No. 12, 2007, pp. 30-36. doi:10.1016/j.jmatprotec.2006.11.222
[13] J. Antoni and R. Randall, “The Spectral Kurtosis: Application to the Vibratory Surveillance and Diagnostics of Rotating Machines,” Mechanical Systems and Signal Processing, Vol. 20, No. 2, 2006, pp. 308-331. doi:10.1016/j.ymssp.2004.09.002
[14] L. Li and L. Qu, “Cyclic Statistics in Rolling Bearing Diagnosis,” Journal of Sound and Vibration, Vol. 267, No. 2, 2003, pp. 253-265. doi:10.1016/S0022-460X(02)01412-8
[15] W. Wang, F. Ismail and F. Golnaraghi, “A Neuro-Fuzzy Approach for Gear System Monitoring,” IEEE Transactions on Fuzzy Systems, Vol. 12, No. 5, 2004, pp. 710-723. doi:10.1109/TFUZZ.2004.834807
[16] K. Al-Raheem, A. Roy, K. Ramachandran, D. Harrison and S. Grainger, “Application of the Laplace-Wavelet Combined with ANN for Rolling Bearing Fault Diagnosis,” Journal of Vibration and Acoustics, Vol. 130, 2008, pp. 1-9. doi:10.1115/1.2948399
[17] H. Ocak and K. Loparo, “HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings,” Journal of Vibration and Acoustics, Vol. 127, No. 4, 2005, pp. 299-306. doi:10.1115/1.1924636
[18] Q. Sun and Y. Tang, “Singularity Analysis Using Continuous Wavelet Transform for Bearing Fault Diagnosis,” Mechanical Systems and Signal Processing, Vol. 16, No. 6, 2002, pp. 1025-1041. doi:10.1006/mssp.2002.1474
[19] C. Wang and R. Gao, “Wavelet Transforms with Spectral Post-Processing for Enhanced Feature Extraction,” IEEE Transactions on Instrumentation and Measurement, Vol. 52, No. 4, 2003, pp. 1296-1301. doi:10.1109/TIM.2003.816807
[20] J. Liu, W. Wang and F. Golnaraghi, “An Extended Wavelet Spectrum for Bearing Fault Diagnostics,” IEEE Transactions on Instrumentation and Measurement, Vol. 57, No. 12, 2008, pp. 2801-2812. doi:10.1109/TIM.2008.927211
[21] C. Croux, G. Dhaene and D. Hoorelbeke, “Testing the Information Matrix Equality with Robust Estimators,” Journal of Statistical Planning and Inference, Vol. 136, No. 10, 2006, pp. 3583-3613. doi:10.1016/j.jspi.2005.02.021
[22] Y. Gel, W. Miao and J. Gastwirth, “Robust Directed Tests of Normality against Heavy-Tailed Alternatives,” Computational Statistics & Data Analysis, Vol. 51, No. 5, 2007, pp. 2734-2746. doi:10.1016/j.csda.2006.08.022

  
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