{"title":"Multiple-Points Fault Signature's Dynamics Modeling for Bearing Defect Frequencies","authors":"Muhammad F. Yaqub, Iqbal Gondal, Joarder Kamruzzaman","volume":59,"journal":"International Journal of Mechanical and Mechatronics Engineering","pagesStart":2541,"pagesEnd":2547,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/15172","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.<\/p>\r\n","references":"[1] J. Morel, \"Vibratory monitoring and predictive maintenance,\"\r\nTechniques de l-Ing\u00e9nieur, Measurement and Control, vol. RD, 2002.\r\n[2] M. El Hachemi Benbouzid, \"A review of induction motors signature analysis as a medium for faults detection,\" IEEE Trans. on Ind. Electron,\r\nvol. 47, pp. 984-993, 2000.\r\n[3] Q. Hu, Z. He, Z. Zhang, and Y. Zi, \"Fault diagnosis of rotating\r\nmachinery based on improved wavelet package transform and SVMs\r\nensemble,\" Mechanical Systems and Signal Processing, vol. 21, pp. 688-\r\n705, 2007.\r\n[4] K. Teotrakool, M. J. Devaney, and L. Eren, \"Adjustable-Speed Drive\r\nBearing-Fault Detection Via Wavelet Packet Decomposition,\" IEEE\r\nTrans. on Instrum. and Meas., vol. 58, pp. 2747-2754, 2009.\r\n[5] J. R. Stack, T. G. Habetler, and R. G. Harley, \"Fault-signature modeling\r\nand detection of inner-race bearing faults,\" IEEE Trans. on Industry\r\nApplications, vol. 42, pp. 61-68, 2006.\r\n[6] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, \"Machine Fault Severity\r\nEstimation Based on Adaptive Wavelet Nodes Selection and SVM\r\n(Accepted for publication),\" in IEEE International Conference on\r\nMechatronics and Automation, China, 2011.\r\n[7] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, \"Severity Invariant\r\nMachine Fault Diagnosis (Accepted for publication),\" in IEEE\r\nInternational Conference on Industrial Electronics and Application,\r\nChina, 2011.\r\n[8] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, \"Resonant Frequency\r\nBand Estimation using Adaptive Wavelet Decomposition Level\r\nSelection (Accepted for publication),\" in IEEE International Conference\r\non Mechatronics and Automation, China, 2011.\r\n[9] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, \"Severity Invariant\r\nFeature Selection for Machine Health Monitoring,\" International Review\r\nof Electrical Egnineering, vol. 6, pp. 238-248, 2011.\r\n[10] M. F. Yaqub, I. Gondal, and J. Kamruzzaman, \"Machine Health\r\nMonitoring Based on Stationary Wavelet Transform and 4th Order\r\nCumulants (Accepted for publication),\" Australian Journal of Electrical\r\n& Electronics Engineering, 2011.\r\n[11] P. D. McFadden and J. D. Smith, \"The vibration produced by multiple\r\npoint defects in a rolling element bearing,\" Journal of Sound and\r\nVibration, vol. 98, pp. 263-273, 1985.\r\n[12] J. Kleer and B. C. Williams, \"Diagnosiing Multiple Faults,\" Artificial\r\nIntelligence, vol. 32, pp. 97-130, 1987 1987.\r\n[13] E. Cabal-Yepez, R. Saucedo-Gallaga, A. G. Garcia-Ramirez, A. A.\r\nFernandez-Jaramillo, M. Pena-Anaya, and M. Valtierra-Rodriguez,\r\n\"FPGA-Based Online Detection of Multiple-Combined Faults through\r\nInformation Entropy and Neural Networks,\" International Conference\r\non Reconfigurable Computing and FPGAs (ReConFig), 2010, pp. 244-\r\n249.\r\n[14] P. W. Tse, Y. H. Peng, and R. Yam, \"Wavelet Analysis and Envelope\r\nDetection For Rolling Element Bearing Fault Diagnosis---Their\r\nEffectiveness and Flexibilities,\" Journal of Vibration and Acoustics, vol.\r\n123, pp. 303-310, 2001.\r\n[15] R. B. Randall, J. Antoni, and S. Chobsaard, \"The relationship between\r\nspectral correlation and envelope analysis in the diagnosis of bearing\r\nfaults and other cyclostationary machine signals,\" Mechanical Systems\r\nand Signal Processing, vol. 15, pp. 945-962, 2001.\r\n[16] I. S. Bozchalooi and M. Liang, \"A joint resonance frequency estimation\r\nand in-band noise reduction method for enhancing the detectability of\r\nbearing fault signals,\" Mechanical Systems and Signal Processing, vol.\r\n22, pp. 915-933, 2008.\r\n[17] P. M. Lerman, \"Fitting Segmented Regression Models by Grid Search,\"\r\nJournal of the Royal Statistical Society. Series C (Applied Statistics),\r\nvol. 29, pp. 77-84, 1980.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 59, 2011"}