In this work, size and shape characteristics of wear debris particles present in the transmission system of heavy earth moving machines have been analysed. Four oil samples were collected from the dumper transmission system for wear debris analysis. Ferrogram maker has been used to seperate the wear particles present in oil samples and to capture the image of particles. The statistical results confirm that the Weibull probability distribution function has more potential to fit the wear particle size distribution accurately than the Rayleigh model in case of heavy earth moving machines. Results show the initiation of abnormal wears and suggested that condition-based maintenance is necessary.
MacianVTormosBOlmedaPet al.Analytical approach to wear rate determination for internal combustion engine condition monitoring based on oil analysis. Tribol Int2003; 36: 771–776.
2.
FengSFanBMaoJet al.Prediction on wear of a spur gearbox by on-line wear debris concentration monitoring. Wear2015; 336: 1–8.
3.
RaadnuiS. Wear particle analysis—utilization of quantitative computer image analysis: a review. Tribol Int2005; 38: 871–878.
4.
WuTWuHDuYet al.Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features. Wear2014; 316: 19–29.
5.
JardineAKLinDBanjevicD. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing2006; 20(7): 1483–1510.
6.
CarrMJWangW. A case comparison of a proportional hazards model and a stochastic filter for condition-based maintenance applications using oil-based condition monitoring information. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability2008; 222(1): 47–55.
7.
WilliamsPAClarkeIC. Understanding polyethylene wear mechanisms by modeling of debris size distributions. Wear2009; 267(1): 646–652.
8.
FangZPattersonBRTurnerME. Modeling particle size distributions by the Weibull distribution function. Materials characterization1993; 31(3): 177–182.
9.
PocockG. Particle size analysis for machinery health monitoring. Nature1977; 270(5636): 414–415.
10.
ZhanmingJHeshengS. Computer image analysis of abrasion debris. Wear1992; 154(1): 1–10.
11.
RoylanceBJWilliamsJADwyer-JoyceR. Wear debris and associated wear phenomena—fundamental research and practice. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology2000; 214(1): 79–105.
12.
LevinRI. Statistics for management, Pearson Education India, 2011.
13.
RinneH. The Weibull distribution: a handbook, CRC Press, 2008.
14.
HoggRVCraigAT. Introduction to mathematical statistics, 5th ed. Upper Saddle River, New Jersey: Prentice Hall, 1995.
15.
RoylanceBJPocockG. Wear studies through particle size distribution I: Application of the Weibull distribution to ferrography. Wear1983; 90(1): 113–136.
16.
KececiogluD. Reliability and life testing handbook, DEStech Publications, Inc, 2002.
17.
UhlířováTPabstWGregorováEHostašaJ. Stereology of dense polycrystalline materials—from interface density and mean curvature integral density to Rayleigh distributions of grain sizes. Journal of the European Ceramic Society2016; 36(9): 2319–2328.
18.
KarCMohantyAR. Multistage gearbox condition monitoring using motor current signature analysis and Kolmogorov–Smirnov test. Journal of Sound and Vibration2006; 290(1): 337–368.
19.
HallLDMbaD. Acoustic emissions diagnosis of rotor-stator rubs using the KS statistic. Mechanical systems and signal processing2004; 18(4): 849–868.
20.
NetterJKutnerMHNachtsheimCJWassermannW. Applied linear statistical models, Chicago: Irwin, 1996.
21.
KirkTBPanzeraDAnamalayRAXuZL. Computer image analysis of wear debris for machine condition monitoring and fault diagnosis. Wear1995; 181: 717–722.
22.
RoylanceBJRaadnuiS. The morphological attributes of wear particles—their role in identifying wear mechanisms. Wear1994; 175(1-2): 115–121.
23.
PengZKirkTBXuZL. The development of three-dimensional imaging techniques of wear particle analysis. Wear1997; 203–204: 418–424.
