The diagnostic method of rolling bearing in planetary gearbox operating at variable load
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AGH University of Science and Technology, Department of Mechanics and Vibroacoustics
Submission date: 2019-05-18
Final revision date: 2019-07-05
Acceptance date: 2019-08-06
Online publication date: 2019-08-12
Publication date: 2019-08-12
Corresponding author
Pawel Pawlik   

AGH University of Science and Technology, Department of Mechanics and Vibroacoustics
Diagnostyka 2019;20(3):69-77
Diagnostics of machines operating at variable loads has been widely described in literature. The methods of analysing the vibroacoustic signals generated by such machines have been developed since the 1980s. They involve a synchronous sampling of signals which carry diagnostic information, where the sampling frequency depends on the machine rotational speed. Presently, there are many methods in the literature used for synchronization of signals with rotational speed based on signal decimation, subsampling or Gabor transform. However, these methods do not totally solve the problem of diagnosing the machines operating at various loads. The change of machine load also affects the amplitudes of diagnostic parameters. The paper attempts to develop a diagnostic method that is independent of the system load change. The method is based on parameterization of amplitudes of characteristic orders. Single-number statistical parameters have been proposed for diagnostics of rolling bearings operating at variable loads. Tests have been conducted on a laboratory rig where the tested object was a rolling bearing on an output shaft of a planetary gearbox. The bearing was damaged by removing the grease which is a frequent type of damage in industry and can lead to a quick bearing seizure.
Tandon N, Choudhury A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International. 2000; 32(1999):469–80.
Zhou L, Duan F, Corsar M, Elasha F, Mba D. A study on helicopter main gearbox planetary bearing fault diagnosis. Applied Acoustics. 2019; 147: 4–14.
Klausen A, Robbersmyr KG, Karimi HR. Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum. IFAC-PapersOnLine. 2017; 50(1):13378–83.
Baydar N, Ball A. Detection of gear failures via vibration and acoustic signals using wavelet transform. Mechanical Systems and Signal Processing. 2003 Jul 1; 17(4):787–804.
Batko W, Barański R. Wavelet Transfer Function in the Analysis of the Influence of a Palm Grip on Actual Vibrations of an Upper Limb. International Journal of Occupational Safety and Ergonomics. 2007; 13(4): 355–365.
Antoni J, Randall RB. The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing. 2006;20(2):308–31.
Randall RB, Antoni J. Rolling element bearing diagnostics-A tutorial. Mechanical Systems and Signal Processing. 2011;25(2):485–520.
Wang Y, Tse PW, Tang B, Qin Y, Deng L, Huang T. Kurtogram manifold learning and its application to rolling bearing weak signal detection. Measurement: Journal of the International Measurement Confederation. 2018;127(4):533–45.
Wodecki J, Michalak A, Zimroz R. Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings. Mechanical Systems and Signal Processing. 2018;102:102–16.
Yu K, Lin TR, Tan J, Ma H. An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis. Measurement: Journal of the International Measurement Confederation. 2019;134:375–84.
Cioch W, Krzyworzeka P. Vibration analysis of running-up turbine engine GTD-350. Diagnostyka. 2007; 4(44): 125–130.
Burdzik R, Konieczny Ł, Warczek J, Cioch W. Adapted linear decimation procedures for TFR analysis of non-stationary vibration signals of vehicle suspensions. Mechanics Research Communications. 2017; 82:29–35.
Zimroz R, Bartkowiak A. Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions. Mechanical Systems and Signal Processing. 2013; 38(1): 237–247.
Pawlik P, Lepiarczyk D, Dudek R, Ottewill JR, Rzeszuciński P, Wójcik M, et al. Vibroacoustic study of powertrains operated in changing conditions by means of order tracking analysis. Eksploatacja i Niezawodnosc – Maintenance and Reliability. 2016; 18(4): 606–612.
Dabrowski D. Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks. Measurement: Journal of the International Measurement Confederation. 2016; 91: 295–308.
Popiołek K, Pawlik P. Diagnosing the technical condition of planetary gearbox using the artificial neural network based on analysis of non-stationary signals. Diagnostyka. 2016; 17(2): 57–64.
Stępień B. A Comparison of Classical and Bayesian Interval Estimation for Long-Term Indicators of Road Traffic Noise. Acta Acustica united with Acustica. 2018; 104(6): 1118–29.
Jaramillo VH, Ottewill JR, Dudek R, Lepiarczyk D, Pawlik P. Condition monitoring of distributed systems using two-stage Bayesian inference data fusion. Mechanical Systems and Signal Processing. 2017; 87: 91–110.
Urbanek J, Barszcz T, Strączkiewicz M, Jablonski A. Normalization of vibration signals generated under highly varying speed and load with application to signal separation. Mechanical Systems and Signal Processing. 2017; 82: 13–31.
National Instruments Corporation. LabVIEW, Order Analysis Toolkit User Manual. 2005.
Xu X, Zhao M, Lin J, Lei Y. Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis. Measurement. 2016; 91: 385–97.
Antoni J. The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing. 2006; 20(2): 282–307.
Pawlik P. Single-number statistical parameters in the assessment of the technical condition of machines operating under variable load. Eksploatacja i Niezawodnosc – Maintenance and Reliability. 2019; 21(1): 164–169.
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