The increasing necessity for machinery availability has emphasized the significance of regular monitoring of machinery health. The associated maintenance program consists of both diagnostic and prognostic elements, making the surveillance of gearboxes crucial to preventing downtime and expensive failures. Various operating conditions, such as load, speed, and environmental factors, inevitably contribute to faults that gradually emerge. The methods for detecting these faults include both experimental observation and simulation of fault behavior under diverse operating conditions. This study offers an exhaustive analysis of different techniques used to process the data collected from gearboxes, including vibration, wear debris, and oil degradation. It also includes various dynamic models, providing a thorough review of experimental and theoretical research related to gearbox failure. Based on the above experimental and theoretical analysis, the suggestions for future research in the domain are enlisted.
SytaAJonakJJedlińskiŁ, et al. Failure diagnosis of a gear box by recurrences. J Vib Acoust2012; 134(4): 041006.
2.
BartelmusW.Gearbox damage process. In: Journal of Physics: Conference Series, vol. 305, no. 1, p. 012029. IOP Publishing.
3.
DaviesA. Handbook of condition Monitoring: techniques and methodology. Dordrecht: Springer Science+Business Media, B.V., 1998.
4.
DavisJR (ed.). Gear materials, properties, and manufacturing. Materials Park, OH: ASM international, 2005.
5.
JardineAKSLinDBanjevicD. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process2006; 20(7): 1483–1510.
6.
LiangXZuoMJFengZ.Dynamic modeling of gearbox faults: a review. Mech Syst Signal Process2018; 98: 852–876.
7.
HiraniHAthreKBiswasS.Comprehensive design methodology for an engine journal bearing. Proc IMechE Part J: J Engineering Tribology2000; 214(4): 401–412.
8.
HiraniHAthreKBiswasS.Rapid and globally convergent method for dynamically loaded journal bearing design. Proc IMechE Part J: J Engineering Tribology1998; 212(3): 207–214.
9.
FlodinAAnderssonS.Simulation of mild wear in spur gears. Wear1997; 207: 16–23.
10.
MuniyappaAChandramohanSSeethapathyS.Detection and diagnosis of gear tooth wear through metallurgical and oil analysis. Tribol Online2010; 5(2): 102–110.
11.
AmarnathMLeeSK.Assessment of surface contact fatigue failure in a spur geared system based on the tribological and vibration parameter analysis. Meas J Int Meas Confed2015; 76: 32–44.
12.
RandallRB. Vibration-based condition monitoring: industrial, automotive and aerospace applications. Chichester,West Sussex, PO: John Wiley & Sons2011.
13.
MohammedODRantataloMAidanpääJO.Dynamic modelling of a one-stage spur gear system and vibration-based tooth crack detection analysis. Mech Syst Signal Process2015; 54: 293–305.
14.
ParkerRG.GuoYEritenelT, et al. Vibration propagation of gear dynamics in a gear-bearing-housing system using mathematical modeling and finite element analysis (No. NASA/CR-2012-217664), NASA, Glenn Research Center, Cleveland, Ohio. 2012.
15.
LoutridisSTrochidisA.Classification of gear faults using hoelder exponents. Mech Syst Signal Process2004; 18(5): 1009–1030.
16.
WangWJMcFaddenPD.Decomposition of gear motion signals and its application to gearbox diagnostics. J Vib Acoust2008; 117(3A): 363.
17.
HöhnBRMichaelisKOttoHP.Minimised gear lubrication by a minimum oil/air flow rate. Wear2009; 266(3–4): 461–467.
18.
KolekarASOlverAVSworskiAE, et al. Windage and churning effects in dipped lubrication. J Tribol2014; 136(2): 021801.
19.
KrantzTLKahramanA. An experimental investigation of the influence of the lubricant viscosity and additives on gear wear. Tribol Trans2004; 47(1): 138–148.
ShengS.Monitoring of wind turbine gearbox condition through oil and wear debris analysis: a full-scale testing perspective. Tribol Trans2016; 59(1): 149–162.
22.
EbnerMYilmazMLohnerT, et al. On the effect of starved lubrication on elastohydrodynamic (EHL) line contacts. Tribol Int2018; 118(June): 515–523.
23.
FitchJ, Noria Corporation. The benefits of utilizing wear debris analysis in industrial machinery, Machinery Lubrication, 2016, pp. 1–8.
24.
OswaldFLinHLiouCH, et al. Dynamic analysis of spur gears using computer program DANST. In 29th Joint Propulsion Conference and Exhibit, Monterey, California, 1993 June, p. 2295.
25.
AmarnathMSujathaC.Surface contact fatigue failure assessment in spur gears using lubricant film thickness and vibration signal analysis. Tribol Trans2015; 58(2): 327–336.
26.
WangSWuTWuH, et al. Modeling wear state evolution using real-time wear debris features. Tribol Trans2017; 60(6): 1022–1032.
27.
CumminsRADoyleEDRebecchiB.Wear damage to spur gears. Wear1974; 27(1): 115–120.
28.
ScottD.Debris examination — a prognostic approach to failure prevention. Wear2003; 34(1): 15–22.
29.
KumarPHiraniHAgrawalAK.Online condition monitoring of misaligned meshing gears using wear debris and oil quality sensors. Ind Lubr Tribol2018; 70(4): 645–655.
30.
ShahHHiraniH.Online condition monitoring of spur gears. Int J Cond Monit2014; 4(1): 15–22.
31.
LoutasTHRouliasDPaulyE, et al. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mech Syst Signal Process2011; 25(4): 1339–1352.
32.
FengSFanBMaoJ, et al. Prediction on wear of a spur gearbox by on-line wear debris concentration monitoring. Wear2015; 336–337: 1–8.
33.
HiraniH. Condition monitoring of high speed gears using vibration and oil analysis, Therm. Fluid Manuf Sci2012: 21–28.
34.
Mimoza NaseskaZitnikM2016. Fourier Transform Infrared Spectroscopy. Seminar Faculty of Mathematics and Physics, university of Ljubljana, pp. 1–12.
35.
KhabouMTBouchaalaNChaariF, et al. Study of a spur gear dynamic behavior in transient regime. Mech Syst Signal Process2011; 25(8): 3089–3101.
36.
SaghafiAFarshidianfarA.An analytical study of controlling chaotic dynamics in a spur gear system. Mech Mach Theory2016; 96: 179–191.
37.
ParkerRGVijayakarSMImajoT.Non-linear dynamic response of a spur gear pair: modelling and experimental comparisons. J Sound Vib2000; 237(3): 435–455.
38.
BeggCDByingtonCSMaynardKP.Dynamic simulation of mechanical fault transition. Proc 54th Meet Soc Mach Fail Prev Technol. Virginia Beach, VA, 2000, pp. 203–212.
39.
KunduPKulkarniSMDarpeKA.A hybrid prognosis approach for life prediction of gears subjected to progressive pitting failure mode. J Intell Manuf2021; 34: 1325–1346.
40.
MohammedOD. Dynamic modelling and vibration analysis for gear tooth crack detection, . Doctoral dissertation, Luleå tekniska universitet, 2015.
41.
SchererM. Vibration health monitoring of gears. MTech Thesis, California Polytechnic State University. 2012.
42.
KuPM.Gear failure modes—importance of lubrication and mechanics. ASLE Trans1976; 19(3): 239–249.
43.
HöhnBRMichaelisK.Influence of oil temperature on gear failures. Tribol Int2004; 37(2): 103–109.
44.
LoutridisSJ.Self-similarity in vibration time series: application to gear fault diagnostics. J Vib Acoust2008; 130(3): 031004.
45.
MaHZengJFengR, et al. Review on dynamics of cracked gear systems. Eng Fail Anal2015; 55: 224–245.
46.
MaRChenYCaoQ.Research on dynamics and fault mechanism of spur gear pair with spalling defect. J Sound Vib2012; 331(9): 2097–2109.
47.
KrambergerJŠramlMGlodežS, et al. Computational model for the analysis of bending fatigue in gears. Comput Struct2004; 82(23–26): 2261–2269.
48.
EndoHRandallRBGosselinC. Differential diagnosis of spall vs. Cracks in the gear tooth fillet region: experimental validation. Mech Syst Signal Process2009; 23(3): 636–651.
49.
CaiJHanXHuaL, et al. Study on stress intensity factors for crack on involute spur gear tooth. Adv Mech Eng2015; 7(3): 1–12.
50.
YauEBusbyHRHouserDR.A rayleigh-ritz approach to modeling bending and shear deflections of gear teeth. Comput Struct1994; 50(5): 705–713.
51.
ChaariFFakhfakhTHaddarM.Analytical modelling of spur gear tooth crack and influence on gearmesh stiffness. Eur J Mech A/Solids2009; 28(3): 461–468.
52.
MohammedODRantataloMKumarU.Analytical crack propagation scenario for gear teeth and time-varying gear mesh stiffness. World Acad Sci Eng Technol2012; 68: 1332–1337.
53.
CoyJJTownsendDPZaretskyEV.Dynamic capacity and surface fatigue life for spur and helical gears. J Lubr Technol2010; 98(2): 267.
54.
GlodezSRenZFlasïJ.Surface fatigue of gear teeth flanks. Stress Int J Biol Stress2000; 73(1999): 475–483.
55.
ZhuDRenNWangQJ.Pitting life prediction based on a 3D line contact mixed ehl analysis and subsurface von mises stress calculation. J Tribol2009; 131(4): 041501.
56.
AmarnathMSujathaCSwarnamaniS.Experimental studies on the effects of reduction in gear tooth stiffness and lubricant film thickness in a spur geared system. Tribol Int2009; 42(2): 340–352.
57.
BartzWJKrijgerV.Pitting fatigue of gears - some ideas on appearance, mechanism and lubricant influence. Tribology1973; 6: 191–195.
58.
TownsendDPZaretskyEVScibbeHW.Lubricant and additive effects on spur gear fatigue life. J Synth Lubr1989; 6(2): 83–106.
59.
KumarPHiraniHAgrawalA.Fatigue failure prediction in spur gear pair using AGMA approach. Mater Today Proc2017; 4(2): 2470–2477.
60.
KrantzTLAlanouMPEvansHP, et al. Surface fatigue lives of case-carburized gears with an improved surface finish. J Tribol2002; 123(4): 709.
61.
LiS.An investigation on the influence of misalignment on micro-pitting of a spur gear pair. Tribol Lett2015; 60(3): 1–12.
62.
SaxenaAChoukseyMPareyA.Effect of mesh stiffness of healthy and cracked gear tooth on modal and frequency response characteristics of geared rotor system. Mech Mach Theory2017; 107(October2016): 261–273.
63.
BlakeJWChengHS.A surface pitting life model for spur gears: Part I—failure probability prediction. J Tribol2008; 113(4): 719.
64.
BlakeJWChengHS.A surface pitting life model for spur gears: Part II—failure probability prediction. J Tribol2008; 113(4): 719.
65.
RamamurtiVVijayendraNHSujathaC.Static and dynamic analysis of spur and bevel gears using fem. Mech Mach Theory1998; 33(8): 1177–1193.
66.
KunduPDarpeAKKulkarniMS.A correlation coefficient based vibration indicator for detecting natural pitting progression in spur gears. Mech Syst Signal Process2019; 129: 741–763.
67.
OgnjanovicM.Progressive gear teeth wear and failure probability modeling. Tribol Ind2004; 26(3–4): 44–49.
68.
LiSKahramanA.A fatigue model for contacts under mixed elastohydrodynamic lubrication condition. Int J Fatigue2011; 33(3): 427–436.
69.
SpitasVCostopoulosTSpitasC.Fast modeling of conjugate gear tooth profiles using discrete presentation by involute segments. Mech Mach Theory2007; 42(6); 751–762.
GuerineAEl HamiAWalhaL, et al. Dynamic response of a spur gear system with uncertain parameters. J Theor Appl Mech2016; 0: 1039.
72.
McFaddenPD.Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration. J Vibr Acoust1986; 108(2): 165–170.
73.
McFaddenPD.Examination of a technique for the early detection of failure in gears by signal processing of the time domain average of the meshing vibration. Mech Syst Signal Process1987; 1(2): 173–183.
74.
GoyalDVanraj PablaBS, et al. Condition monitoring parameters for fault diagnosis of fixed axis gearbox: a review. Arch Comput Methods Eng2017; 24(3): 543–556.
75.
BartelmusWZimrozR.A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mech Syst Signal Process2009; 23(5): 1528–1534.
76.
DalpiazGRivolaARubiniR.Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears. Mech Syst Signal Process2000; 14(3): 387–412.
77.
WidodoASatrijoDPrahastoT, et al. Fault detection of gearbox using time-frequency method. In: AIP Conference Proceedings2017, vol. 1831.
78.
HalimEBShoukat ChoudhuryMAAShahSL, et al. Time domain averaging across all scales: a novel method for detection of gearbox faults. Mech Syst Signal Process2008; 22(2): 261–278.
79.
SharmaVPareyA.Gear crack detection using modified tsa and proposed fault indicators for fluctuating speed conditions. Meas J Int Meas Confed2016; 90: 560–575.
80.
NanadicNArdisPHoodA, et al. Comparative study of vibration condition indicators for detecting cracks in spur gears. In: American Helicopter Society (AHS) 69th Annual Forum (No. GRC-E-DAA-TN8754), 2003.
81.
DempseyPJ2000. A comparison of vibration and oil debris gear damage detection methods applied to pitting damage. Nasa/Tm-2000-210371, 2000 (September). P. 18.
82.
CerradaMZuritaGCabreraD, et al. Fault diagnosis in spur gears based on genetic algorithm and random forest. Mech Syst Signal Process2016; 70–71: 87–103.
83.
IgbaJAlemzadehKDurugboC, et al. Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes. Renew Energy2016; 91: 90–106.
84.
VermaAZhangZKusiakA.Modeling and prediction of gearbox faults with data-mining algorithms. J Sol Energy Eng2013; 135(3): 031007.
85.
PareyAEl BadaouiMGuilletF, et al. Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect. J Sound Vib2006; 294(3): 547–561.
86.
SaravananNCholairajanSRamachandranKI.Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique. Expert Syst Appl2009; 36(2 PART 2): 3119–3135.
87.
DempseyPJ.Integrating oil debris and vibration measurements for intelligent machine health monitoring. University of Toledo. 2002.
88.
WangYXiangJMarkertR, et al. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications. Mech Syst Signal Process2016; 66–67; 679–698.
89.
ChoyFXuAPolyshchukV. Development of a model based technique for gear diagnostics using the winger-ville method, NASA (No. NAS 1.26: 207572).
90.
DempseyPJMosherMHuffEM. Threshold assessment of gear diagnostic tools on flight and test rig data. In: 59th Annual Forum and Technology Display (No. E-13812), Phoenix, Arizona, May 2003.
91.
HuCSmithWARandallRB, et al. Development of a gear vibration indicator and its application in gear wear monitoring. Mech Syst Signal Process2016; 76–77: 319–336.
92.
ZiaranSDarulaR.Determination of the state of wear of high contact ratio gear sets by means of spectrum and cepstrum analysis. J Vib Acoust2013; 135(2): 021008.
93.
ZhangRGuXGuF, et al. Gear wear process monitoring using a sideband estimator based on modulation signal bispectrum. Appl Sci2017; 7(3): 274.
94.
UrbanekJBarszczTStrączkiewiczM, et al. Normalization of vibration signals generated under highly varying speed and load with application to signal separation. Mech Syst Signal Process2017; 82: 13–31.
95.
TengWDingXZhangX, et al. Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform. Renew Energy2016; 93: 591–598.
96.
AbboudDBaudinSAntoniJ, et al. The spectral analysis of cyclo-non-stationary signals. Mech Syst Signal Process2016; 75: 280–300.
97.
ZhuLDingHZhuXY.Synchronous averaging of time-frequency distribution with application to machine condition monitoring. J Vib Acoust2007; 129(4): 441.
98.
MosherMPryorAHLewickiDG. Detailed vibration analysis of pinion gear with time frequency methods. Electromagn. Interconnections, (June2003), pp. 189–217.
99.
StaszewskiWJTomlinsonGR.Local tooth fault detection in gearboxes using a moving window procedure. Mech Syst Signal Process1997; 11(3): 331–350.
100.
ChoyFKMuglerDHZhouJ.Damage identification of a gear transmission using vibration signatures. J Mech Des2003; 125(2): 394.
101.
StaszewskiWJWordenKTomlinsonGR.Time–frequency analysis in gearbox fault detection using the wigner–ville distribution and pattern recognition. Mech Syst Signal Process1997; 11(5): 673–692.
102.
LoutridisSJ.Instantaneous energy density as a feature for gear fault detection. Mech Syst Signal Process2006; 20(5): 1239–1253.
103.
WangWKannegD.An integrated classifier for gear system monitoring. Mech Syst Signal Process2009; 23(4): 1298–1312.
104.
LiZJiangYHuC, et al. Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: a review. Meas J Int Meas Confed2016; 90: 4–19.
105.
PengZKChuFL.Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process2004; 18(2): 199–221.
106.
RafieeJTsePW.Use of autocorrelation of wavelet coefficients for fault diagnosis. Mech Syst Signal Process2009; 23(5): 1554–1572.
107.
YoshidaAOhueYIshikawaH.Diagnosis of tooth surface failure by wavelet transform of dynamic characteristics. Tribol Int2000; 33(3–4): 273–279.
108.
FanXZuoMJ.Gearbox fault detection using hilbert and wavelet packet transform. Mech Syst Signal Process2006; 20(4): 966–982.
109.
LinJZuoMJ.Extraction of periodic components for gearbox diagnosis combining wavelet filtering and cyclostationary analysis. J Vib Acoust2004; 126(3): 449.
110.
LiHZhangYZhengH.Application of hermitian wavelet to crack fault detection in gearbox. Mech Syst Signal Process2011; 25(4): 1353–1363.
111.
MarkWD.Time-synchronous-averaging of gear-meshing-vibration transducer responses for elimination of harmonic contributions from the mating gear and the gear pair. Mech Syst Signal Process2015; 62: 21–29.
112.
RaadAAntoniJSidahmedM.Indicators of cyclostationarity: theory and application to gear fault monitoring. Mech Syst Signal Process2008; 22(3): 574–587.
113.
CoatsMDRandallRB.Single and multi-stage phase demodulation based order-tracking. Mech Syst Signal Process2014; 44(1–2): 86–117.
114.
PanMCLinYF.Further exploration of vold-kalman-filtering order tracking with shaft-speed information-II: engineering applications. Mech Syst Signal Process2006; 20(6): 1410–1428.
115.
ChengWZhangZLeeS, et al. Source contribution evaluation of mechanical vibration signals via enhanced independent component analysis. J Manuf Sci Eng2012; 134(2): 021014.
116.
TumerIYHuffEM.Analysis of triaxial vibration data for health monitoring of helicopter gearboxes. J Vib Acoust2003; 125(1): 120.
117.
LiBZhangXWuJ.New procedure for gear fault detection and diagnosis using instantaneous angular speed. Mech Syst Signal Process2017; 85: 415–428.
118.
RicciRPennacchiP.Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mech Syst Signal Process2011; 25(3): 821–838.
119.
WuTYChenJCWangCC.Characterization of gear faults in variable rotating speed using hilbert-huang transform and instantaneous dimensionless frequency normalization. Mech Syst Signal Process2012; 30: 103–122.
120.
MarkWDLeeHPatrickR, et al. A simple frequency-domain algorithm for early detection of damaged gear teeth. Mech Syst Signal Process2010; 24(8): 2807–2823.
121.
VillaLFReñonesAPeránJR, et al. Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load. Mech Syst Signal Process2012; 29: 436–446.
122.
AbbesMSTriguiMChaariF, et al. Dynamic behaviour modelling of a flexible gear system by the elastic foundation theory in presence of defects. Eur J Mech A/Solids2010; 29(5): 887–896.
123.
MarkWDHinesJA.Frequency-domain assessment of gear-tooth bending-fatigue damage-progression using the average-log-ratio, ALR, algorithm. Mech Syst Signal Process2014; 45(2): 479–487.
124.
LiYDingKHeG, et al. Vibration mechanisms of spur gear pair in healthy and fault states. Mech Syst Signal Process2016; 81: 183–201.
125.
YesilyurtIGuFBallAD.Gear tooth stiffness reduction measurement using modal analysis and its use in wear fault severity assessment of spur gears. NDT E Int2003; 36(5): 357–372.
126.
DingHKahramanA.Interactions between nonlinear spur gear dynamics and surface wear. J Sound Vib2007; 307(3–5): 662–679.
127.
YuWMechefskeCKTimuskM.The dynamic coupling behaviour of a cylindrical geared rotor system subjected to gear eccentricities. Mech Mach Theory2017; 107(April2016): 105–122.
128.
AnderssonS.Initial wear of gears. Tribol Int1977; 10(4): 206–210.
129.
IwaiYHondaTMiyajimaT, et al. Quantitative estimation of wear amounts by real time measurement of wear debris in lubricating oil. Tribol Int2010; 43(1–2): 388–394.
130.
WangSWuTWuH, et al. Modeling wear state evolution using real-time wear debris features. TribolTrans2017; 60(6): 1022–1032.
131.
KhanMAStarrAG.Wear debris: basic features and machine health diagnostics. insight non-destructive test. Cond Monit2006; 48(8): 470–476.
132.
StachowiakGPStachowiakGWPodsiadloP.Automated classification of wear particles based on their surface texture and shape features. Tribol Int2008, 41(1): 34–43.
133.
MyshkinNKKwonOKGrigorievAY, et al. Classification of wear debris using a neural network. Wear1997; 203–204(96): 658–662.
134.
PengZKessissoglouN.An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear2003; 255(7–12); 1221–1232.
135.
HiraniH. Online wear monitoring of spur gears. Indian J Tribol2009; 4(2): 38–43.
136.
DempseyPJLewickiDGDeckerHJ. Investigation of gear and bearing fatigue damage using debris particle distributions. Nasa/Tm - 2004-212883 Arl-Tr-3133, 2004, vol. 298(0704), p. 16.
137.
PodsiadloPStachowiakGW.The development of the modified hurst orientation transform for the characterization of surface topography of wear particles. Tribol Lett1998; 4: 215–229.
138.
LaghariMSMemonQAKhuwajaGA.Knowledge based wear particle analysis. Int J Inf Technol2004; 1(3): 91–95.
139.
MyshkinNKKongHGrigorievAY, et al. The use of color in wear debris analysis. Wear2001; 250(251): 1218–1226.
140.
FitchB, 2018. Anatomy of wear debris. Machinery Lubrication, pp. 1–7.
141.
TanCKIrvingPMbaD.A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears. Mech Syst Signal Process2007; 21(1): 208–233.
142.
HamiltonAQuailF.Detailed state of the art review for the different online/inline oil analysis techniques in context of wind turbine gearboxes. J Tribol2011; 133(4): 044001.
143.
PodsiadloPStachowiakGW.Evaluation of boundary fractal methods for the characterization of wear particles. Wear1998; 217(1): 24–34.
144.
VähäojaPVälimäkiIRoppolaK, et al. Wear metal analysis of oils. Crit Rev Anal Chem2008; 38(2): 67–83.
145.
HennebergMJørgensenBEriksenRL.Oil condition monitoring of gears onboard ships using a regression approach for multivariate T2 control charts. J Process Control2016; 46: 1–10.
146.
FanBLiBFengS, et al. Modeling and experimental investigations on the relationship between wear debris concentration and wear rate in lubrication systems. Tribol Int2017; 109(28): 114–123.
147.
WatsonH.5 Wear of gears. Tribology1969; 2(4): 212–216.
148.
SondhiyaOGuptaA.Wear debris analysis of automotive engine lubricating oil using by ferrography. Int j Eng Innov Technol2012; 2(5), 46–54.
149.
WuJMaoJCaoW, et al. Characterization of wear-debris group in on-line visual ferrographic images. Proc IMechE Part J: J Eng Tribol2014; 228(11): 1298–1307.
150.
YanLYoubaiXFangZ, et al. Revision to the concept of equilibrium concentration of particles in lubrication system of machines. Wear1998; 215(1–2): 205–210.
151.
SarkarAD.The role of wear debris in the study of wear. Wear1983; 90(1): 39–47.
152.
RoylanceBJ.Wear debris and associated wear phenomena-fundamental research and practice. Proc Inst Mech Eng Part J J Eng Tribol2000; 214(1): 79–105.
153.
RaadnuiS.Wear particle analysis - utilization of quantitative computer image analysis: a review. Tribol Int2005; 38(10): 871–878.
154.
YuanWChinKSHuaM, et al. Shape classification of wear particles by image boundary analysis using machine learning algorithms. Mech Syst Signal Process2016; 72–73: 346–358.
155.
ChoUTichyJA.Quantitative correlation of wear debris morphology: grouping and classification. Tribol Int2000; 33(7): 461–467.
156.
LaghariMS.Recognition of texture types of wear particles. Neural Comput Appl2003; 12(1): 18–25.
157.
MyshkinNKGrigorievAY.Morphology: texture, shape and color of friction surfaces and wear debris in tribodiagnostics problems. J Frict Wear2008; 29(3): 192–199.
158.
RoylanceBJRaadnuiS.The morphological attributes of wear particles - their role in identifying wear mechanisms. Wear1994; 175(1–2): 115–121.
159.
PengZKirkTB.Wear particle classification in a fuzzy grey system. Wear1999; 225–229(PART II): 1238–1247.
160.
StachowiakGW.Particle angularity and its relationship to abrasive and erosive wear. Wear2000; 241(2): 214–219.
161.
PengZ.An integrated intelligence system for wear debris analysis. Wear2002; 252(9–10): 730–743.
162.
PengZKirkTB.Computer image analysis of wear particles in three-dimensions for machine condition monitoring. Wear1998; 223(1–2): 157–166.
163.
KirkTBPanzeraDAnamalayRV, et al. Computer image analysis of wear debris for machine condition monitoring and fault diagnosis. Wear1995; 181–183(PART 2): 717–722.
164.
PodsiadloPStachowiakGW.Scale-invariant analysis of wear particle surface morphology I: theoretical background, computer implementation and technique testing. Wear2000; 242(1–2): 160–179.
165.
PodsiadloPStachowiakGW. Scale-invariant analysis of wear particle surface morphology II. Fractal dimension. Wear2000; 242(1–2): 180–188.
166.
StachowiakGW.Numerical characterization of wear particles morphology and angularity of particles and surfaces. Tribol Int1998; 31(1–3): 139–157.
167.
TianYWangJPengZ, et al. A new approach to numerical characterisation of wear particle surfaces in three-dimensions for wear study. Wear2012; 282–283: 59–68.
168.
LiuHMaoKZhuC, et al. Mixed Lubricated Line contact analysis for spur gears using a deterministic model. J Tribol. 2012; 134(2): 021501.
169.
AkbarzadehSKhonsariMM.Prediction of steady state adhesive wear in spur gears using the EHL load sharing concept. J Tribol. 2009; 131(2): 024503.
170.
MasjediMKhonsariMM.On the prediction of steady-state wear rate in spur gears. Wear2015; 342–343: 234–243.
171.
TownsendDPShimskiJ. Evaluation of the EHL film thickness and extreme pressure additives on gear surface fatigue life. NASA Tech. Memo., 1994, pp. 0–9.
172.
TownsendDPZaretskyEV.Effect of five lubricants on life of AISI 9310 spur gears. NASA STI/Recon Tech Rep N, 1985; vol. 85(June1991), p. 16099.
173.
KrantzTL.On the correlation of specific film thickness and gear pitting life. Gear Technol2015; (February): 52–62.
174.
BrandãoJACerqueiraPSeabraJHO, et al. Measurement of mean wear coefficient during gear tests under various operating conditions. Tribol Int2016; 102: 61–69.
175.
HiraniHJangraDSidhKN.Experimental investigation on the wear performance of nano-additives on degraded gear lubricant. Lubricants2023; 11(2): 51.
176.
SidhKNJangraDHiraniH.An experimental investigation of the tribological performance and dispersibility of 2D nanoparticles as oil additives. Lubricants2023; 11(4): 179.
177.
HiraniHJangraDSidhKN.Experimental analysis of chemically degraded lubricant’s impact on spur gear wear. Lubricants2023; 11(5): 201.
KhangNVCauTMDienNP.Modelling parametric vibration of gear-pair systems as a tool for aiding gear fault diagnosis. Tech Mech2004; 24: 198–205.
189.
VelexPMaatarM.A mathematical model for analyzing the influence of shape deviation and mounting errors on gear dynamic behaviour. J Sound Vibr1996; 191: 629–660.
190.
MacLennanLD.An analytical method to determine the influence of shape deviation on load distribution and mesh stiffness for spur gears. Proc Inst Mech Eng Part C J Mech Eng Sci2002; 216(10): 1005–1016.
191.
LiS.Effects of machining errors, assembly errors and tooth modifications on loading capacity, load-sharing ratio and transmission error of a pair of spur gears. Mech Mach Theory2007; 42(6): 698–726.
192.
MoradiHSalariehH.Analysis of nonlinear oscillations in spur gear pairs with approximated modelling of backlash nonlinearity. Mech Mach Theory2012; 51: 14–31.
193.
SmithJD.Effects of dynamics in gear tooth contact. Tribol Int1980; 13(3): 133–135.
194.
LiS.Effects of misalignment error, tooth modifications and transmitted torque on tooth engagements of a pair of spur gears. Mech Mach Theory2015; 83: 125–136.
195.
LitvinFLFuentesAHawkinsM, et al. Design, generation and tooth contact analysis (TCA) of asymmetric face gear drive with modified geometry. Nasa/Tm 2001-210614 Arl-Tr-2373, 2001, vol. 190(43), pp. 5837–5865.
196.
BonoriGBarbieriMPellicanoF.Optimum profile modifications of spur gears by means of genetic algorithms. J Sound Vib2008; 313(3–5): 603–616.
197.
HeSGundaRSinghR.Effect of sliding friction on the dynamics of spur gear pair with realistic time-varying stiffness. J Sound Vib2007; 301(3–5): 927–949.
198.
LinHHTownsendDPOswaldFB.Profile modification to minimize spur gear dynamic loading, NASA, (No. NAS 1.15: 89901), 1987.
199.
HotaitMAKahramanA.Experiments on the relationship between the dynamic transmission error and the dynamic stress factor of spur gear pairs. Mech Mach Theory2013; 70: 116–128.
200.
LiuLPinesDJ.The influence of gear design parameters on gear tooth damage detection sensitivity. J Mech Des2002; 124(4): 794.
201.
FaggioniMSamaniFSBertacchiG, et al. Dynamic optimization of spur gears. Mech Mach Theory2011; 46(4): 544–557.
202.
MargolinWNorthW.A parametric study of spur gear bending strength. SAE Tech Pap Ser, 2010, vol. 1(January1998).
203.
PadmasolalaGLinHHOswaldFB. Influence of tooth spacing errors on gears with and without profile modifications. In: 8th Int Power Transm Gearing Conf, Baltimore, Maryland, (September 2000), pp. 647–654.
204.
MeineKSchneiderTSpaltmannD, et al. The influence of roughness on friction. Wear2002; 253(7–8): 725–732.
205.
OswaldFLinHLiouC-H, et al. Dynamic analysis of spur gears using computer program DANST. In: 29th Joint Propulsion Conference and Exhibit, Monterey, California, June 1993, (p. 2295).
206.
StehmanSVOvertonWSUnitB, et al. Effect of extended tooth contact on the modeling of spur gear transmissions. In: 29th Joint propulsion conference and exhibit, Monterey, California, pp. 743–748.
207.
VaishyaMSinghR.Strategies for modeling friction in gear dynamics. J Mech Des2003; 125(2): 383.
208.
ChowdhurySYedavalliRK.Dynamics of low speed geared shaft systems mounted on rigid bearings. Mech Mach Theory2017; 112: 123–144.
209.
Diez-IbarbiaAFernandez-del-RinconAde-JuanA, et al. Frictional power losses on spur gears with tip reliefs. The friction coefficient role. Mech Mach Theory2018; 121: 15–27.
210.
Fernandez-del-RinconAGarciaPDiez-IbarbiaA, et al. Enhanced model of gear transmission dynamics for condition monitoring applications: effects of torque, friction and bearing clearance. Mech Syst Signal Process2017; 85: 445–467.
211.
LiuGParkerRG.Impact of tooth friction and its bending effect on gear dynamics. J Sound Vib2009; 320(4–5): 1039–1063.
212.
MaHPangXFengR, et al. Evaluation of optimum profile modification curves of profile shifted spur gears based on vibration responses. Mech Syst Signal Process2016; 70–71: 1131–1149.
213.
Sainte-MarieNVelexPRouloisG, et al. A study on the correlation between dynamic transmission error and dynamic tooth loads in spur and helical gears. J Vib Acoust2016; 139(1): 011001.
214.
VelexPAjmiM.On the modelling of excitations in geared systems by transmission errors. J Sound Vib2006; 290(3–5): 882–909.
215.
KangHXiongYWangT, et al. Research on the dynamic response of high-contact-ratio spur gears influenced by surface roughness under EHL condition. Appl Surf Sci2017; 392: 8–18.
216.
PodzharovESyromyatnikovVPonce NavarroJP, et al. Static and dynamic transmissin error in spur gears. Open Ind Manuf Eng J2008; 1(1): 37–41.
217.
Nevzat ÖzgüvenHHouserDR. Mathematical models used in gear dynamics—a review. Top Catal1988; 121(3): 383–411.
218.
BartelmusW. Mathematical modelling and computer simulations as an aid to gearbox diagnostics. Mech Syst Signal Process2001; 15(5): 855–871.
219.
KorkaZIMituletuIC.A review of dynamic models used in simulation of gear transmissions. Analele Univ “Eftimie Murgu” Reşiła2014; 2014(XXI): 165–174.
220.
FernándezAIglesiasMDe-JuanA, et al. Gear transmission dynamic: effects of tooth profile deviations and support flexibility. Appl Acoust2014; 77: 138–149.
221.
ShenYYangSLiuX.Nonlinear dynamics of a spur gear pair with time-varying stiffness and backlash based on incremental harmonic balance method. Int J Mech Sci2006; 48(11): 1256–1263.
222.
KarpatFDoganOYuceC, et al. An improved numerical method for the mesh stiffness calculation of spur gears with asymmetric teeth on dynamic load analysis. Adv Mech Eng2017; 9(8): 1–12.
AkbarzadehSKhonsariMM.Performance of spur gears considering surface roughness and shear thinning lubricant. J Tribol2008; 130(2): 021503.
226.
ZhouCXiaoZChenS, et al. Normal and tangential oil film stiffness of modified spur gear with non-newtonian elastohydrodynamic lubrication. Tribol Int2017; 109(December2016): 319–327.
227.
ZhouCXiaoZ.Stiffness and damping models for the oil film in line contact elastohydrodynamic lubrication and applications in the gear drive. Appl Math Model2018; 61: 634–649.
228.
CooleyCGLiuCDaiX, et al. Gear tooth mesh stiffness: a comparison of calculation approaches. Mech Mach Theory2016; 105: 540–553.
229.
LiangXZhangHLiuL, et al. The influence of tooth pitting on the mesh stiffness of a pair of external spur gears. Mech Mach Theory2016; 106: 1–15.
230.
Abouel-seoudSDyabEElmorsyM.Influence of tooth pitting and cracking on gear meshing stiffness and dynamic response of wind turbine gearbox. Mech Eng2012; 2(3): 151–165.
231.
MaHLiZFengM, et al. Time-varying mesh stiffness calculation of spur gears with spalling defect. Eng Fail Anal2016; 66: 166–176.
232.
WanZCaoHZiY, et al. An improved time-varying mesh stiffness algorithm and dynamic modeling of gear-rotor system with tooth root crack. Eng Fail Anal2014; 42: 157–177.
233.
WuSZuoMJPareyA.Simulation of spur gear dynamics and estimation of fault growth. J Sound Vib2008; 317(3–5): 608–624.
234.
MaHSongRPangX, et al. Time-varying mesh stiffness calculation of cracked spur gears. Eng Fail Anal2014; 44: 179–194.
235.
MohammedODRantataloM.Dynamic response and time-frequency analysis for gear tooth crack detection. Mech Syst Signal Process2016; 66–67: 612–624.
236.
ChenZZhaiWShaoY, et al. Analytical model for mesh stiffness calculation of spur gear pair with non-uniformly distributed tooth root crack. Eng Fail Anal2016; 66: 502–514.
237.
ZhouCChenCGuiL, et al. A nonlinear multi-point meshing model of spur gears for determining the face load factor. Mech Mach Theory2018; 126, 210–224.
238.
ParkDHashashYMA. Viscous damping formulation & high frequency motion propagation in non-linear site response analysis. Proc OceanMTSIEEE2005; 22: 1–6.
239.
YangDCHLinJY. Hertzian damping, tooth friction and bending elasticity in gear impact dynamics. J Mech Transm Autom Des2010; 109(2): 189.
LiuFHTheodossiadesSBergmanLA, et al. Analytical characterization of damping in gear teeth dynamics under hydrodynamic conditions. Mech Mach Theory2015; 94: 141–147.
242.
LiuXYangYZhangJ. Investigation on coupling effects between surface wear and dynamics in a spur gear system. Tribol Int2016; 101: 383–394.
243.
ImrekHDüzcükoǧluH.Relation between wear and tooth width modification in spur gears. Wear2007; 262(3–4): 390–394.
244.
KumarPHiraniHAgrawalAK.Effect of gear misalignment on contact area: theoretical and experimental studies. Measurement2019; 132: 359–368.
245.
FlodinA.Wear of spur and helical gears. Doctoral dissertation, Department of Machine Design, Royal Institute of Technology, 2000, pp. 1–33.
246.
GuilbaultRLalondeS.Early diagnostic of concurrent gear degradation processes progressing under time-varying loads. Mech Syst Signal Process2016; 76–77: 337–352.
247.
KumarPHiraniHAgrawalAK.Modeling and simulation of mild wear of spur gear considering radial misalignment. Iran J Sci Technol Trans Mech Eng 9; 43(Suppl 1): 107–116.
248.
JangraDHiraniHDarpeAK.Effect of combined (radial-axial-angular direction) misalignment on sliding wear of spur gears: a comprehensive study. Tribol Int2023; 189: 108908.
249.
WuSChengHS.Sliding wear calculation in spur gears. J Tribol2008; 115(3): 493.
250.
WilliamsJA.Wear modelling: analytical, computational and mapping: a continuum mechanics approach. Wear1999; 225–229(I): 1–17.
251.
WilliamsJA.Wear and wear particles - some fundamentals. Tribol Int2005; 38(10): 863–870.
252.
KahramanADingH.A methodology to predict surface wear of planetary gears under dynamic conditions. Mech Based Des Struct Mach2010; 38(4): 493–515.
253.
ZmitrowiczA.Wear patterns and laws of wear – a review. J Theor Appl Mech2006; 44(2): 219–253.
254.
BajpaiPKahramanAAndersonNE. A surface wear prediction methodology for parallel-axis gear pairs. J Tribol2004; 126(3): 597.
255.
ZhaoFTianZLiangX, et al. An integrated prognostics method for failure time prediction of gears subject to the surface wear failure mode. IEEE Trans Reliab2018; 67(1): 316–327.
256.
JanakiramanVLiSKahramanA.An investigation of the impacts of contact parameters on wear coefficient. J Tribol2014; 136(3): 031602.
257.
FlodinA.Wear investigation of spur gear teeth. TriboTest2000; 7(1): 45–60.
258.
PõdraPAnderssonS.Wear simulation with the winkler surface model. Wear1997; 207(1–2): 79–85.
259.
KuangJHLinAD.The Effect of tooth wear on the vibration spectrum of a spur gear pair. J Vib Acoust2001; 123(3): 311.
260.
YangLJ.A test methodology for the determination of wear coefficient. Wear2005; 259(7–12): 1453–1461.
261.
MaHFengMLiZ, et al. Time-varying mesh characteristics of a spur gear pair considering the tip-fillet and friction. Meccanica2017; 52: 1695–1709.
262.
WanZCaoHZiY, et al. Mesh stiffness calculation using an accumulated integral potential energy method and dynamic analysis of helical gears. Mech Mach Theory2015; 92: 447–463.
263.
Del RinconAFViaderoFIglesiasM, et al. A model for the study of meshing stiffness in spur gear transmissions. Mech Mach Theory2013; 61: 30–58.
264.
PandyaYPareyA.Experimental investigation of spur gear tooth mesh stiffness in the presence of crack using photoelasticity technique. Eng Fail Anal2013; 34: 488–500.
265.
RaghuwanshiNKPareyA.Mesh stiffness measurement of cracked spur gear by photoelasticity technique. Measurement2015; 73: 439–452.
266.
RaghuwanshiNKPareyA.Experimental measurement of gear mesh stiffness of cracked spur gear by strain gauge technique. Measurement2016; 86: 266–275.
267.
ChabertGTranTDMathisR. An evaluation of stresses and deflection of spur gear teeth under strainASME J Eng Ind1974; 96(1): 85–93.
268.
OmarFKMoustafaKAEmamS.Mathematical modeling of gearbox including defects with experimental verification. J Vibr Control2012; 18(9): 1310–1321.
269.
ChoyFKPolyshchukVZakrajsekJJ, et al. Analysis of the effects of surface pitting and wear on the vibration of a gear transmission system. Tribol Int1996; 29(1): 77–83.
270.
Torregrosa KochJPMolina VicuñaC. Dynamic and phenomenological vibration models for failure prediction on planet gears of planetary gearboxes. J Brazilian Soc Mech Sci Eng2014; 36: 533–545.
271.
MaRChenY.Research on the dynamic mechanism of the gear system with local crack and spalling failure. Eng Fail Anal2012; 26: 12–20.
272.
HammamiARinconADRuedaFV, et al. Modal analysis of back-to-back planetary gear: experiments and correlation against lumped-parameter model. J Theor Appl Mech2015; 53(1): 125–138.
273.
MabroukIBEl HamiAWalhaL, et al. Dynamic vibrations in wind energy systems: application to vertical axis wind turbine. Mech Syst Signal Process2017; 85: 396–414.
274.
InalpolatMKahramanA.A dynamic model to predict modulation sidebands of a planetary gear set having manufacturing errors. J Sound Vibr2010; 329(4): 371–393.
275.
GuoYParkerRG.Dynamic modeling and analysis of a spur planetary gear involving tooth wedging and bearing clearance nonlinearity. Eur J Mech-A/Solids2010; 29(6): 1022–1033.
276.
LiuGParkerRG. Dynamic modeling and analysis of tooth profile modification for multimesh gear vibrationASME J Mech Des; 130(12): 121402.
277.
VanhollebekeFPeetersPHelsenJ, et al. Large scale validation of a flexible multibody wind turbine gearbox model. J Comput Nonlinear Dyn2015; 10(4): 041006.
278.
BartelmusWChaariFZimrozR, et al. Modelling of gearbox dynamics under time-varying nonstationary load for distributed fault detection and diagnosis. Eur J Mech-A/Solids2010; 29(4): 637–646.
279.
MaHPangXFengR, et al. Fault features analysis of cracked gear considering the effects of the extended tooth contact. Eng Fail Anal2015; 48: 105–120.
280.
LiGLiFWangY, et al. Fault diagnosis for a multistage planetary gear set using model-based simulation and experimental investigation. Shock Vibr2016; 2016(1): 1–19.
281.
OttewillJRRuszczykABrodaD.Monitoring tooth profile faults in epicyclic gearboxes using synchronously averaged motor currents: mathematical modeling and experimental validation. Mech Syst Signal Process2017; 84: 78–99.
282.
DiehlEJTangJ.Predictive modeling of a two-stage gearbox towards fault detection. Shock Vibr2016; 2016(1): 1–13.
283.
AlmqvistTAlmqvistALarssonR.A comparison between computational fluid dynamic and reynolds approaches for simulating transient EHL line contacts. Tribol Int2004; 37(1): 61–69.
284.
Fernandez-Del-RinconADiez-IbarbiaATheodossiadesS.Gear transmission rattle: assessment of meshing forces under hydrodynamic lubrication. Appl Acoust2019; 144: 85–95.
285.
BarbieriMLubrechtAAPellicanoF.Behavior of lubricant fluid film in gears under dynamic conditions. Tribol Int2013; 62: 37–48.
286.
LiSKahramanA.A transient mixed elastohydrodynamic lubrication model for spur gear pairs. J Tribol2009; 132(1): 011501.
287.
OuyangTHuangHZhangN, et al. A model to predict tribo-dynamic performance of a spur gear pair. Tribol Int2017; 116(July): 449–459.
288.
FangYHeJHuangP.Experimental and numerical analysis of soft elastohydrodynamic lubrication in line contact. Tribol Lett2017; 65(2): 1–11.
289.
LiSKahramanA.A micro-pitting model for spur gear contacts. Int J Fatigue2014; 59: 224–233.
290.
LiSKahramanA.Influence of dynamic behaviour on elastohydrodynamic lubrication of spur gears. Proc IMechE, Part J: J Eng Tribol2011; 225(8): 740–753.
291.
SatoMTakanashiS.Thermo-elastohydrodynamic lubrication of an involute gear. Tribol Int1982; 15(1): 23–30.
292.
DongHLHuJBLiXY.Temperature analysis of involute gear based on mixed elastohydrodynamic lubrication theory considering tribo-dynamic behaviors. J Tribol2014; 136(2): 021504.
293.
OuyangTHuangHZhouX, et al. A finite line contact tribo-dynamic model of a spur gear pair. Tribol Int2018; 119(September2017): 753–765.
294.
LiSAnisettiA.A tribo-dynamic contact fatigue model for spur gear pairs. Int J Fatigue2017; 98: 81–91.
295.
LiSKahramanA.A tribo-dynamic model of a spur gear pair. J Sound Vib2013; 332(20): 4963–4978.
