Revised computed order tracking method for element rolling bearing diagnosis

Computed order tracking is a kind of algorithm which is generally used to eliminate the effect of varying rotational speed on the rotary machines. However, it is impropriate to use the computed order tracking algorithm and its corresponding analysis techniques on the fault diagnosis of the rolling element bearing unless the peak time of the fault induced impulse is set as zero which cannot be met in the real engineering. In this way, the resampling process which is realized by padding different amount of data into the segments with different rotational speed will cause change of intervals between the adjacent impulse peaks in the angle domain and then affect the final diagnosis by envelope analysis. In this paper, a quantitative analysis is firstly carried on to detect the mechanism of this phenomenon and find key influence factors. And then a change in envelope interval based compensation algorithm is put forward to eliminate this kind of effect.

[1]  Robert X. Gao,et al.  Envelope deformation in computed order tracking and error in order analysis , 2014 .

[2]  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 .

[3]  James B. Boyer,et al.  An Editorial Statement , 1981, Annals of the History of Computing.

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

[5]  Jian Yong Li,et al.  The Error Caused by Order Analysis for Rolling Bearing Fault Signal , 2013 .

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

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

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

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

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

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

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

[13]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

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

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

[16]  W. Marsden I and J , 2012 .

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

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

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

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

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

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

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

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

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

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