Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32759
Multiple-Points Fault Signature's Dynamics Modeling for Bearing Defect Frequencies

Authors: Muhammad F. Yaqub, Iqbal Gondal, Joarder Kamruzzaman

Abstract:

Occurrence of a multiple-points fault in machine operations could result in exhibiting complex fault signatures, which could result in lowering fault diagnosis accuracy. In this study, a multiple-points defect model (MPDM) is proposed which can simulate fault signature-s dynamics for n-points bearing faults. Furthermore, this study identifies that in case of multiple-points fault in the rotary machine, the location of the dominant component of defect frequency shifts depending upon the relative location of the fault points which could mislead the fault diagnostic model to inaccurate detections. Analytical and experimental results are presented to characterize and validate the variation in the dominant component of defect frequency. Based on envelop detection analysis, a modification is recommended in the existing fault diagnostic models to consider the multiples of defect frequency rather than only considering the frequency spectrum at the defect frequency in order to incorporate the impact of multiple points fault.

Keywords: Envelop detection, machine defect frequency, multiple faults, machine health monitoring.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084526

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2206

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] P. D. McFadden and J. D. Smith, "The vibration produced by multiple point defects in a rolling element bearing," Journal of Sound and Vibration, vol. 98, pp. 263-273, 1985.
[12] J. Kleer and B. C. Williams, "Diagnosiing Multiple Faults," Artificial Intelligence, vol. 32, pp. 97-130, 1987 1987.
[13] E. Cabal-Yepez, R. Saucedo-Gallaga, A. G. Garcia-Ramirez, A. A. Fernandez-Jaramillo, M. Pena-Anaya, and M. Valtierra-Rodriguez, "FPGA-Based Online Detection of Multiple-Combined Faults through Information Entropy and Neural Networks," International Conference on Reconfigurable Computing and FPGAs (ReConFig), 2010, pp. 244- 249.
[14] 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.
[15] R. B. Randall, J. Antoni, and S. Chobsaard, "The relationship between spectral correlation and envelope analysis in the diagnosis of bearing faults and other cyclostationary machine signals," Mechanical Systems and Signal Processing, vol. 15, pp. 945-962, 2001.
[16] 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.
[17] P. M. Lerman, "Fitting Segmented Regression Models by Grid Search," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 29, pp. 77-84, 1980.