Abrasion Modeling of Multiple-Point Defect Dynamics for Machine Condition Monitoring

Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.

[1]  Iqbal Gondal,et al.  Multiple-points fault signature's dynamics modeling for bearing defect frequencies , 2011 .

[2]  Mohamed Benbouzid,et al.  A review of induction motors signature analysis as a medium for faults detection , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[3]  Iqbal Gondal,et al.  Inchoate Fault Detection Framework: Adaptive Selection of Wavelet Nodes and Cumulant Orders , 2012, IEEE Transactions on Instrumentation and Measurement.

[4]  L. S. Qu,et al.  Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform , 2004 .

[5]  Kenneth A. Loparo,et al.  Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data , 2004 .

[6]  P. Lerman.  Fitting Segmented Regression Models by Grid Search , 1980 .

[7]  I. S. Bozchalooi,et al.  A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals , 2008 .

[8]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[9]  Iqbal Gondal,et al.  An Adaptive Self-Configuration Scheme for Severity Invariant Machine Fault Diagnosis , 2013, IEEE Transactions on Reliability.

[10]  Bin Zhang,et al.  A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection , 2011, IEEE Transactions on Industrial Electronics.

[11]  Jianbo Yu,et al.  Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment , 2012, IEEE Transactions on Industrial Electronics.

[12]  T.G. Habetler,et al.  Fault-signature modeling and detection of inner-race bearing faults , 2006, IEEE Transactions on Industry Applications.

[13]  M. F. Yaqub,et al.  Resonant frequency band estimation using adaptive wavelet decomposition level selection , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[14]  C. Davidson,et al.  Effect of Abrasion Medium on Wear of Stress-bearing Composites and Amalgam in vitro , 1986, Journal of dental research.

[15]  P. D. McFadden,et al.  The vibration produced by multiple point defects in a rolling element bearing , 1985 .

[16]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

[17]  Lei Guo,et al.  Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector Machine , 2009 .

[18]  T.G. Habetler,et al.  Motor bearing damage detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[19]  Yaoyu Li,et al.  A review of recent advances in wind turbine condition monitoring and fault diagnosis , 2009, 2009 IEEE Power Electronics and Machines in Wind Applications.

[20]  Thomas G. Habetler,et al.  A survey of condition monitoring and protection methods for medium voltage induction motors , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[21]  Eduardo Cabal-Yepez,et al.  FPGA-Based Online Detection of Multiple-Combined Faults through Information Entropy and Neural Networks , 2010, 2010 International Conference on Reconfigurable Computing and FPGAs.

[22]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[23]  Michael J. Devaney,et al.  Adjustable Speed Drive Bearing Fault Detection via Wavelet Packet Decomposition , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[24]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[25]  J. Kamruzzaman,et al.  An Adaptive Self-Configuration Scheme for Severity Invariant Machine Fault Diagnosis , 2013, IEEE Transactions on Reliability.

[26]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[27]  H. Wilman,et al.  An experimental study of friction and wear during abrasion of metals , 1960, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[28]  Iqbal Gondal,et al.  Envelope-Wavelet Packet Transform for Machine Condition Monitoring , 2011 .

[29]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

[30]  M. F. Yaqub,et al.  Severity invariant machine fault diagnosis , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[31]  Xiang Li,et al.  Data-driven approaches in health condition monitoring — A comparative study , 2010, IEEE ICCA 2010.

[32]  Dejie Yu,et al.  Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings , 2005 .

[33]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[34]  Iqbal Gondal,et al.  Severity invariant feature selection for machine health monitoring , 2011 .

[35]  K. F. Martin,et al.  A review by discussion of condition monitoring and fault diagnosis in machine tools , 1994 .