Envelope-Wavelet Packet Transform for Machine Condition Monitoring

Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the feature vector. This increase in the dimensionality has the tendency to 'over-fit' the training data and could mislead the fault diagnostic model. In this paper a novel technique, envelope wavelet packet transform (EWPT) is proposed in which features are extracted based on wavelet packet transform of the filtered envelope signal rather than the overall vibration signal. It not only reduces the computational overhead in terms of reduced number of wavelet decomposition levels and features but also improves the fault detection accuracy. Analytical expressions are provided for the optimal frequency resolution and decomposition level selection in EWPT. Experimental results with both actual and simulated machine fault data demonstrate significant gain in fault detection ability by EWPT at reduced complexity compared to existing techniques.

Authors:

• M. F. Yaqub
• I. Gondal
• J. Kamruzzaman

Keywords:

• Envelope Detection
• Wavelet Transform
• Bearing Faults
• Machine Health Monitoring.

References:

[1] J. Morel, "Vibratory monitoring and predictive maintenance,"
Techniques de l-Ingénieur, Measurement and Control, vol. RD, 2002.
[2] M. El Hachemi Benbouzid, "A review of induction motors signature
analysis as a medium for faults detection," IEEE Trans. on Ind.
Electron., vol. 47, pp. 984-993, 2000.
[3] Q. Hu, Z. He, Z. Zhang, and Y. Zi, "Fault diagnosis of rotating
machinery based on improved wavelet package transform and SVMs
ensemble," Mechanical Systems and Signal Processing, vol. 21, pp. 688-
705, 2007.
[4] K. Teotrakool, M. J. Devaney, and L. Eren, "Adjustable-Speed Drive
Bearing-Fault Detection Via Wavelet Packet Decomposition," IEEE
Trans. on Instrum. and Meas., vol. 58, pp. 2747-2754, 2009.
[5] J. R. Stack, T. G. Habetler, and R. G. Harley, "Fault-signature modeling
and detection of inner-race bearing faults," IEEE Trans. on Industry
Applications., vol. 42, pp. 61-68, 2006.
[6] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, "Machine Fault Severity
Estimation Based on Adaptive Wavelet Nodes Selection and SVM
(Accepted for publication)," in IEEE International Conference on
Mechatronics and Automation, China, 2011.
[7] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, "Severity Invariant
Machine Fault Diagnosis (Accepted for publication)," in IEEE
International Conference on Industrial Electronics and Application,
China, 2011.
[8] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, "Resonant Frequency
Band Estimation using Adaptive Wavelet Decomposition Level
Selection (Accepted for publication)," in IEEE International Conference
on Mechatronics and Automation, China, 2011.
[9] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, "Severity Invariant
Feature Selection for Machine Health Monitoring," International Review
of Electrical Egnineering, vol. 6, pp. 238-248, 2011.
[10] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, "Machine Health
Monitoring Based on Stationary Wavelet Transform and 4th Order
Cumulants (Accepted for publication)," Australian Journal of Electrical
& Electronics Engineering, 2011.
[11] Z. K. Peng and F. L. Chu, "Application of the wavelet transform in
machine condition monitoring and fault diagnostics: a review with
bibliography," Mechanical Systems and Signal Processing, vol. 18, pp.
199-221, 2004.
[12] L. Eren and M. J. Devaney, "Bearing damage detection via wavelet
packet decomposition of the stator current," IEEE Trans. Instrum. and
Meas., vol. 53, pp. 431-436, 2004.
[13] E. C. C. Lau and H. W. Ngan, "Detection of Motor Bearing Outer
Raceway Defect by Wavelet Packet Transformed Motor Current
Signature Analysis," IEEE Trans. Instrum. and Meas., vol. 59, pp. 2683-
2690, 2010.
[14] G. Y. Yen and L. Kuo-Chung, "Wavelet packet feature extraction for
vibration monitoring," Proceedings of IEEE International Conference
on Control Applications, 1999, pp. 1573-1578 vol. 2.
[15] F. Zhao, J. Chen, and W. Xu, "Condition prediction based on wavelet
packet transform and least squares support vector machine methods,"
Proceedings of the Institution of Mechanical Engineers, Part E: Journal
of Process Mechanical Engineering, vol. 223, pp. 71-79, 2009.
[16] L. Eren, Y. Cekic, and M. J. Devaney, "Enhanced feature selection from
wavelet packet coefficients in fault diagnosis of induction motors with artificial neural networks," in IEEE Instrumentation and Measurement
Technology Conference (I2MTC), 2010, pp. 960-963.
[17] Z. Jianhua, Y. Zhixin, and S. F. Wong, "Machine condition monitoring
and fault diagnosis based on support vector machine," in IEEE
International Conference on Industrial Engineering and Engineering
Management (IEEM), 2010, pp. 2228-2233.
[18] I. S. Bozchalooi and M. Liang, "A joint resonance frequency estimation
and in-band noise reduction method for enhancing the detectability of
bearing fault signals," Mechanical Systems and Signal Processing, vol.
22, pp. 915-933, 2008.
[19] R. B. Randall, J. Antoni, and S. Chobsaard, "The relationship between
spectral correlation and envelope analysis in the diagnossi of bearing
faults and other cyclostationary machine signals," Mechanical Systems
and Signal Processing, vol. 15, pp. 945-962, 2001.
[20] P. W. Tse, Y. H. Peng, and R. Yam, "Wavelet Analysis and Envelope
Detection For Rolling Element Bearing Fault Diagnosis---Their
Effectiveness and Flexibilities," Journal of Vibration and
Acoustics, vol. 123, pp. 303-310, 2001.
[21] J. S. Walker, A Primer on Wavelets and their Scientific Applications.
New York: Chapman &Hall/CRC, 1999.
[22] W. Changting and R. X. Gao, "Wavelet transform with spectral postprocessing
for enhanced feature extraction," in Proceedings of the 19th
IEEE Instrumentation and Measurement Technology Conference, 2002,
pp. 315-320 vol.1.
[23] H. Qiu, J. Lee, J. Lin, and G. Yu, "Wavelet filter-based weak signature
detection method and its application on rolling element bearing
prognostics," Journal of Sound and Vibration, vol. 289, pp. 1066-1090,
2006.
[24] P. Indyk and R. Motwani, "Approximate nearest neighbors: towards
removing the curse of dimensionality," in Proceedings of the thirtieth
annual ACM symposium on Theory of computing, Dallas, Texas, United
States, 1998.
[25] V. Sotiris and M. Pecht, "Support Vector Prognostics Analysis of
Electronic Products and Systems," in AAAI Fall Symposium on Artificial
Intelligence for Prognostics, 2007, pp. 120-127.
[26] V. Vapnik, E. Levin, and Y. L. Cun, "Measuring the VC-dimension of a
learning machine," Neural Comput., vol. 6, pp. 851-876, 1994.
[27] C. W. Hsu, C. C. Chang, and C. J. Lin, A practical guide to Support
Vector Classification: Technical report, Department of Computer
Science and Information Engineering, National Taiwan University,
Taipei., 2003.
[28] H. Ocak and K. A. Loparo, "Estimation of the running speed and bearing
defect frequencies of an induction motor from vibration data,"
Mechanical Systems and Signal Processing, vol. 18, pp. 515-533, 2004.