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TECHNICAL PAPERS

Differential Diagnosis of Gear and Bearing Faults

[+] Author and Article Information
J. Antoni, R. B. Randall

School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney 2052, Australia

J. Vib. Acoust 124(2), 165-171 (Mar 26, 2002) (7 pages) doi:10.1115/1.1456906 History: Received September 01, 2001; Revised December 01, 2001; Online March 26, 2002
Copyright © 2002 by ASME
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References

Burchill, R. F., Frarey, J. L., and Wilson, D. S., 1973, “New Machinery Health Diagnostic Techniques Using High-Frequency Vibration,” SAE paper 730930.
Randall,  R. B., 1982, “A New Method of Modeling Gear Faults,” J. Mech. Des., 104, pp. 259–267.
McFadden,  P. D., 1986, “Detecting Fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration,” ASME J. Vibr. Acoust., 108, pp. 165–170.
Dyer,  D., and Stewart,  R. M., 1978, “Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis,” J. Mech. Des., 100, pp. 229–235.
McFadden,  P. D., and Smith,  J. D., 1984, “Vibration Monitoring of Rolling Element Bearings by the High Frequency Resonance Technique—A Review,” Tribol. Int., 117, No. 1, pp. 3–10.
Randall,  R. B., and Antoni,  J., 2001, “The Relationship between Spectral Correlation and Envelope Analysis in the Diagnostics of Bearing Faults and other Cyclostationary Machine Signals,” Mech. Syst. Signal Process., 15, No. 5, pp. 945–962.
Gardner, W. A., 1986, Introduction to Random Processes With Applications to Signals and Systems, Macmillan.
Antoni, J., and Randall, R. B., 2001, “Optimization of SANC for Separating Gear and Bearing Signals,” Condition Monitoring and Diagnosis Engineering Management Conference, Manchester, UK, pp. 89–96.
Randall, R. B., and Antoni, J., 2001, “Separation of Gear and Bearing Fault Signals in Helicopter Gearboxes,” Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques, 16–18 Oct., Compiègne, FR, pp. 161–183.
McFadden,  P. D., and Smith,  J. D., 1984, “Model for the Vibration Produced by a Single Point Defect in a Rolling Element Bearing,” J. Sound Vib., 96, No. 1, pp. 69–82.
Ho,  D., and Randall,  R. B., 2000, “Optimization of Bearing Diagnostic Techniques Using Simulated and Actual Bearing Fault Signals,” Mech. Syst. Signal Process., 14, No. 5, pp. 763–788.

Figures

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Experiment on test-rig with a cracked tooth. (a) vibration signal (normalized amplitude) (b) synchronous average on 25 cycles (c) residual signal
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Experiment on a test-rig with a faulty bearing (outer-race fault) (a) vibration signal (normalized amplitude) (b) synchronous average on 20 cycles (c) residual signal (signal with synchronous average removed)
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The impacting process and the corresponding expected vibration signal (e.g., acceleration)
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Power spectral density of the time jitter process (Ti−iT) measured on an actual rolling element bearing (inner race fault)
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Power spectrum of the cyclostationary impacting process F(t) with σδ=T/100
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Power spectrum of the pseudo-cyclostationary impacting process F(t) with σΔ=T/50
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Power spectrum of the generating process F(t) of a distributed fault
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Spectral correlation density of a localized inner race fault
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Spectral correlation density of an advanced inner race fault
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Spectral correlation density of a gear signal
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Simulated gear signal modulated by an advanced inner race fault
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Spectral correlation density for α=Ω in the fault-free case
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Spectral correlation density for α=Ω in the faulty case
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Spectral correlation density for α=Ω: dotted line—healthy case; continuous line-rough surface on half the inner race. Tracking on the output shaft.
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Spectral correlation density for α=Ω: dotted line—healthy case; continuous line-slot on the inner race. Tracking on the input shaft.
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Spectral correlation density for α=Ω: dotted line—healthy case; continuous line-inner race fault

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