Envelope deformation in computed order tracking and error in order analysis

Abstract The defect diagnosis of rolling bearings operating under time-varying rotational speeds entails an integrative approach involving envelope analysis and computed order tracking that converts a vibration signal from the time domain into the angle domain to eliminate the effect of speed variations. When a signal is resampled at a constant angular increment, the amount of data padded into each data segment will vary, depending on the rate of change in the rotational speeds. This leads to changes in the distance between the adjacent impulse peaks and consequently the results of order analysis. This effect is particularly prominent when the rate of speed change is significant. This paper presents a quantitative analysis of key factors affecting the accuracy of order analysis on rotating machines under variable speeds. An analytical model is established, simulated, and experimentally evaluated. The effects of speed variation, instantaneous speed, angular interval between impulses, and the peak time of the impulse are specified. It is concluded that the error in the order analysis will increase as the acceleration increases. Furthermore, the error is larger under low speeds.

[1]  Jyoti K. Sinha,et al.  A future possibility of vibration based condition monitoring of rotating machines , 2013 .

[2]  P. N. Saavedra,et al.  Accurate assessment of computed order tracking , 2006 .

[3]  K. R. Fyfe,et al.  ANALYSIS OF COMPUTED ORDER TRACKING , 1997 .

[4]  Mohamed Benbouzid,et al.  A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems , 2009 .

[5]  Yuh-Tay Sheen,et al.  A complex filter for vibration signal demodulation in bearing defect diagnosis , 2004 .

[6]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[7]  Robert B. Randall,et al.  Order tracking for discrete-random separation in variable speed conditions , 2012 .

[8]  Huageng Luo,et al.  Synthesized Synchronous Sampling Technique for Differential Bearing Damage Detection , 2010 .

[9]  Tomasz Barszcz,et al.  Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings , 2014 .

[10]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

[11]  Robert X. Gao,et al.  Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .

[12]  N. Jamaludin.  Monitoring extremely slow rolling element bearings: part II , 2002 .

[13]  Heng Wang,et al.  Anomaly Detection for Equipment Condition via Frequency Spectrum Entropy , 2012 .

[14]  Kesheng Wang,et al.  Application of computed order tracking, Vold–Kalman filtering and EMD in rotating machine vibration , 2011 .

[15]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[16]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[17]  Thomas L Lagö,et al.  Main Principles and Limitations of Current Order Tracking Methods , 2005 .

[18]  Miha Boltežar,et al.  Improved model of a ball bearing for the simulation of vibration signals due to faults during run-up , 2011 .

[19]  Robert X. Gao,et al.  Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis , 2009 .

[20]  Walter Bartelmus.  Object and Operation Factor Oriented Diagnostics , 2012 .

[21]  Robert X. Gao,et al.  A hybrid approach to bearing defect diagnosis in rotary machines , 2012 .