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Overview on Gear Health Prognostics

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Probabilistic Prognostics and Health Management of Energy Systems

Abstract

This chapter is dedicated to an overview of prognostics methods for gear health management. By noticing that most prognostic methods are application dependent and new methods keep emerging, this study is necessary for providing the latest status of prognostics capability specific to gears. The reviewed frameworks and/or methods are grouped into data-driven, physics-based and integrated ones. Their respective merits and drawbacks are outlined. The opportunities and challenges are also discussed for future research.

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References

  1. G. Vachtsevanos, F.L. Lewis, M. Roemer, A. Hess, B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems (Wiley, 2006)

    Google Scholar 

  2. P.D. McFadden, Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration. J. Vib. Acoust. Stress Reliab. Des. 108, 165–170 (1986)

    Article  Google Scholar 

  3. W.J. Wang, P.D. McFadden, Decomposition of gear motion signals and its application to gearbox diagnostics. J. Vib. Acoust. 117, 363–369 (1995)

    Article  Google Scholar 

  4. D. Brie, M. Tomczak, H. Oehlmann, A. Richard, Gear crack detection by adaptive amplitude and phase demodulation. Mech. Syst. Signal Process. 11, 149–167 (1997)

    Article  Google Scholar 

  5. P. Vecer, M. Kreidl, R. Smid, Condition indicators for gearbox condition monitoring systems. Acta Polytechnica 45, 35–43 (2005)

    Google Scholar 

  6. A.K. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20, 1483–1510 (2006)

    Article  Google Scholar 

  7. A. Heng, S. Zhang, A. Tan, J. Mathew, Review—rotating machinery prognostics: state of the art, challenges and opportunities. Mech. Syst. Signal Process. 23, 724–739 (2009)

    Article  Google Scholar 

  8. L. Liao, F. Kottig, Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans. Reliab. 63, 191–207 (2014)

    Article  Google Scholar 

  9. J.W. Hines, A. Usynin, Current computational trends in equipment prognostics. Int. J. Comput. Intell. Syst. 1, 94–102 (2008)

    Article  Google Scholar 

  10. K.L. Tsui, N. Chen, Q. Zhou, Y. Hai, W. Wang, Prognostics and health management: a review on data driven approaches. Math. Probl. Eng. 2015, Article ID 793161, 17 pp. (2015)

    Google Scholar 

  11. E. Zio, Prognostics and health management of industrial equipment, in Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, ed. by S. Kadry (IGI Global, 2012), pp. 333–356

    Google Scholar 

  12. X.S. Si, W. Wang, C.H. Hu, D.H. Zhou, Remaining useful life estimation—a review on the statistical data driven approaches. Eur. J. Oper. Res. 213, 1–14 (2011)

    Article  MathSciNet  Google Scholar 

  13. J.Z. Sikorska, M. Hodkiewicz, L. Ma, Prognostic modeling options for remaining useful life estimation by industry. Mech. Syst. Signal Process. 25, 1803–1836 (2011)

    Article  Google Scholar 

  14. A.K.S. Jardine, A.H.C. Tsang, Maintenance, Replacement, and Reliability: Theory and Applications (CRC Press, 2005)

    Google Scholar 

  15. W. Weibull, A statistical theory of the strength of materials. Ingeniors Vetenskaps Akademiens, Handlingar 151 (1939)

    Google Scholar 

  16. G. Lundberg, A. Palmgren, Dynamic capacity of rolling bearing. Ingeniors Vetenskaps Akademiens, Handlingar 196 (1947)

    Google Scholar 

  17. J.J. Coy, D.P. Townsend, E.V. Zaretsky, Dynamic capacity and surface fatigue life for spur and helical gears. J. Lubr. Technol. 267–274 (1976)

    Google Scholar 

  18. P.C. Paris, F. Erdogen, A critical analysis of crack propagation laws. J. Basic Eng. 85, 528–534 (1963)

    Article  Google Scholar 

  19. H. Liebowitz, E.T. Moyer, Finite element methods in fracture mechanics. Comput. Struct. 31, 1–9 (1989)

    Article  Google Scholar 

  20. R.D. Henshell, K.G. Shaw, Crack tip finite elements are unnecessary. Int. J. Numer. Meth. Eng. 9, 495–507 (1975)

    Article  MATH  Google Scholar 

  21. R.S. Barsoum, On the use of isoparametric finite elements in linear fracture mechanics. Int. J. Numer. Meth. Eng. 10, 25–37 (1976)

    Article  MATH  Google Scholar 

  22. L.B. Sills, D. Sherman, On quarter-point three-dimensional finite elements in linear elastic fracture mechanics. Int. J. Fract. 41, 177–196 (1989)

    Article  Google Scholar 

  23. J.R. Rice, A path independent integral and the approximate analysis of strain concentration by notches and cracks. J. Appl. Mech. 35, 379–386 (1968)

    Article  Google Scholar 

  24. B. Abersek, J. Flasker, Numerical methods for evaluation of service life gear. Int. J. Numer. Meth. Eng. 38, 2531–2545 (1995)

    Article  MATH  Google Scholar 

  25. S. Pehan, T.K. Hellen, J. Flasker, Applying numerical methods for determining the service life of gears. Fatigue Fract. Eng. Mater. Struct. 18, 971–979 (1995)

    Article  Google Scholar 

  26. L.E. Spievak, P.A. Wawrzynek, A.R. Ingraffea, D.G. Lewicki, Simulating fatigue crack growth in spiral bevel gears. Eng. Fract. Mech. 68, 53–76 (2001)

    Article  Google Scholar 

  27. S. Glodez, M. Sraml, J. Kramberger, A computational model for determination of service life of gears. Int. J. Fatigue 24, 1013–1020 (2002)

    Article  MATH  Google Scholar 

  28. J.E. Collipriest, An experimentalist’s view of the surface flaw problem, in The Surface Crack: Physics Problems Compute Solutions, ASME (1972), pp. 43–61

    Google Scholar 

  29. K. Inoue, M. Kato, N. Takatsu, Fracture mechanics based evaluation of strength of carburized gear teeth, in Proceedings of the JSME International Conference on Motion and Power Transmissions (1991), pp. 801–806

    Google Scholar 

  30. M. Guagliano, L. Vergani, Effect of crack closure on gear crack propagation. Int. J. Fatigue 23, 65–73 (2001)

    Article  Google Scholar 

  31. E. Wheeler, Spectrum loading and crack growth. J. Basic Eng. Trans. ASME 94, 181–186 (1972)

    Article  Google Scholar 

  32. S. Pehan, T.K. Hellen, J. Flakder, S. Glodez, Numerical methods for determining stress intensity factors vs crack depth in gear tooth roots. Int. J. Fatigue 19, 677–685 (1997)

    Article  Google Scholar 

  33. D.G. Lewicki, R. Ballarini, Gear crack propagation investigations. Tribotest J. 5, 157–172 (1998)

    Article  Google Scholar 

  34. D.G. Lewicki, R. Ballarini, Rim thickness effects on gear crack propagation life. Int. J. Fract. 87, 59–86 (1997)

    Article  Google Scholar 

  35. D.G. Lewicki, R.F. Handschuh, L.E. Spievak, P.A. Wawrzynek, A.R. Ingraffea, Consideration of moving tooth load in gear crack propagation predictions. Trans. ASME 123, 118–124 (2001)

    Article  Google Scholar 

  36. C. Li, H. Lee, Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mech. Syst. Signal Process. 19, 836–846 (2005)

    Article  Google Scholar 

  37. S. Choi, C.J. Li, Practical gear crack prognosis via gear condition index fusion, gear dynamic simulator, and fast crack growth model. J. Syst. Control Eng. 221, 465–473 (2007)

    Google Scholar 

  38. C.J. Li, H. Lee, S.H. Choi, Estimating size of gear tooth root crack using embedded modeling. Mech. Syst. Signal Process. 16, 841–852 (2002)

    Article  Google Scholar 

  39. S. Choi, C.J. Li, Estimation of gear tooth transverse crack size from vibration by fusing selected gear condition indices. Meas. Sci. Technol. 17, 2395–2400 (2006)

    Article  Google Scholar 

  40. J.F. Archard, Contact and rubbing of flat surface. J. Appl. Phys. 24, 981–988 (1953)

    Article  Google Scholar 

  41. T.F.J. Quinn, Review of oxidation wear, Parts I and II. Tribol. Int. 16, Part I 257–305 and Part II 305–315 (1983)

    Google Scholar 

  42. S. Wu, H.S. Cheng, A sliding wear model for partial-EHL contacts. ASME J. Tribol. 113, 134–141 (1991)

    Article  Google Scholar 

  43. S. Wu, H.S. Cheng, Sliding wear calculation in spur gears. J. Tribol. 115, 493–500 (1993)

    Article  Google Scholar 

  44. A. Flodin, S. Andersson, Simulation of mild wear in spur gears. Wear 207, 16–23 (1997)

    Article  Google Scholar 

  45. A. Flodin, S. Andersson, Simulation of wild wear in helical gears. Wear 241, 123–128 (2000)

    Article  Google Scholar 

  46. P. Bajpai, A. Kahraman, N.E. Anderson, A surface wear prediction methodology for parallel-axis gear pairs. J. Tribol. 126, 597–605 (2004)

    Article  Google Scholar 

  47. F. Zhao, Z. Tian, Y. Zeng, Integrated prognostics method for gear wear prediction. Finished

    Google Scholar 

  48. P. Tse, D. Atherton, Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. J. Vib. Acoust. 121, 355–362 (1999)

    Article  Google Scholar 

  49. W.Q. Wang, M.F. Golnaraghi, F. Ismail, Prognosis of machine health condition using neuro-fuzzy systems. Mech. Syst. Signal Process. 18, 813–831 (2004)

    Article  Google Scholar 

  50. W. Wang, F. Ismail, F. Golnaraghi, Assessment of gear damage monitoring techniques using vibration measurements. Mech. Syst. Signal Process. 15, 905–922 (2001)

    Article  Google Scholar 

  51. W. Wang, An intelligent system for machinery condition monitoring. IEEE Trans. Fuzzy Syst. 16, 110–122 (2008)

    Article  Google Scholar 

  52. B. Samanta, C. Nataraj, Prognostics of machine condition using soft computing. Robot. Comput.-Integr. Manuf. 24, 816–823 (2008)

    Article  Google Scholar 

  53. Z. Tian, M.J. Zuo, Health condition prediction of gears using a recurrent neural network approach. IEEE Trans. Reliab. 59, 700–705 (2010)

    Article  Google Scholar 

  54. X. Zhang, L. Xiao, J. Kang, Degradation prediction model based on a neural network with dynamic windows. Sensors 15, 6996–7015 (2015)

    Article  Google Scholar 

  55. X. Zhang, J. Kang, E. Bechhoefer, J. Zhao, A new feature extraction method for gear fault diagnosis and prognosis. Eksploatacja i Niezawodnosc—Maint. Reliab. 16, 295–300 (2014)

    Google Scholar 

  56. S. Hussain, H.A. Gabbar, Vibration analysis and time series prediction for wind turbine gearbox prognostics. Int. J. Progn. Health Manage. 4, 69–79 (2013)

    Google Scholar 

  57. H. Link, W. LaCava, J. V. Dam, B. McNiff, S. Sheng, R. Wallen, W. McDade, S. Lambert, S. Butterfield, F. Oyague, Gearbox reliability collaborative project report: findings from phase 1 and phase 2 testing. Technical Report, NREL/TP-5000-51885 (2011)

    Google Scholar 

  58. W. Wang, Toward dynamic model-based prognostics for transmission gears, in Proceedings of SPIE, vol. 4733 (2002)

    Google Scholar 

  59. Z. Tian, M.J. Zuo, S. Wu, Crack propagation assessment for spur gears using model-based analysis and simulation. J. Intell. Manuf. 23, 239–253 (2012)

    Article  Google Scholar 

  60. P. Dempsey, R. Handschuh, A. Afjeh, Spiral bevel gear damage detection using decision fusion analysis, NASA/TM-2002-211814 (2002)

    Google Scholar 

  61. Y. Qu, D. He, J. Yoon, B.V. Hecke, J. Zhu, E. Bechhoefer, Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors—a comparative study. Sensors 14, 1372–1393 (2014)

    Article  Google Scholar 

  62. J. Zhu, J. Yoon, D. He, E. Bechhoefer, Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines. Wind Energy 18, 1131–1149 (2015)

    Article  Google Scholar 

  63. R.E. Kalman, A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)

    Article  Google Scholar 

  64. M.S. Arulampalam, S. Maskell, N. Gordon, T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50, 174–188 (2002)

    Article  Google Scholar 

  65. E. Bechhoefer, D. He, A process for data driven prognostics, in Proceedings of the 2012 Conference of the Society for Machinery Failure Prevention Technology (MFPT) (2012), pp. 193–212

    Google Scholar 

  66. D. He, E. Bechhoefer, J. Mam, J. Zhu, A particle filtering based approach for gear prognostics, in Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, Chap 13 (2012)

    Google Scholar 

  67. M. Gasperin, D. Juricic, P. Boskoski, J. Vizintin, Model-based prognostics of gear health using stochastic dynamical models. Mech. Syst. Signal Process. 25, 537–548 (2011)

    Article  Google Scholar 

  68. D. Wang, Q. Miao, Q. Zhou, G. Zhou, An intelligent prognostic system for gear performance degradation assessment and remaining useful life estimation. J. Vib. Acoust. 137, 021004-1–02100402100412 (2015)

    Google Scholar 

  69. G. Kacprzynski, A. Sarlashkar, M. Roemer, A. Hess, B. Hardman, Predicting remaining life by fusing the physics of failure modeling with diagnostics. J. Miner. Metals Mater. Soc. 56, 29–35 (2004)

    Article  Google Scholar 

  70. F. Zhao, Z. Tian, E. Bechhoefer, Y. Zeng, An integrated prognostics method under time-varying operating conditions. IEEE Trans. Reliab. 64, 372–387 (2013)

    Google Scholar 

  71. F. Zhao, Z. Tian, Y. Zeng, A stochastic collocation approach for efficient integrated gear health prognosis. Mech. Syst. Signal Process. 39, 372–387 (2013)

    Article  Google Scholar 

  72. F. Zhao, Z. Tian, Y. Zeng, Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method. IEEE Trans. Reliab. 62, 146–159 (2013)

    Article  Google Scholar 

  73. E. Bechhoefer, D. He P. Dempsey, Gear health threshold setting based on a probability of false alarm, in Annual Conference of the Prognostics and Health Management Society (2011)

    Google Scholar 

  74. J. Qiu., C. Zhang, B. Seth, S.Y. Liang, Damage mechanics approach for bearing lifetime prognostics. Mech. Syst. Signal Process. 16, 817–829 (2002)

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Alberta, Edmonton, Canada

    Fuqiong Zhao & Zhigang Tian

  2. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada

    Yong Zeng

Authors
  1. Fuqiong Zhao
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  2. Zhigang Tian
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  3. Yong Zeng
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Correspondence to Fuqiong Zhao .

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Editors and Affiliations

  1. Department of Mechanical Engineering, Texas Tech University , Lubbock, Texas, USA

    Stephen Ekwaro-Osire

  2. Faculdade de Engenharia de Ilha Solteira, Universidade Estadual Paulista , Centro, Ilha Solteira, São Paulo, Brazil

    Aparecido Carlos Gonçalves

  3. School of Engineering and Computer Science and Mathematics, West Texas A&M University, Canyon, Texas, USA

    Fisseha M. Alemayehu

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Zhao, F., Tian, Z., Zeng, Y. (2017). Overview on Gear Health Prognostics. In: Ekwaro-Osire, S., Gonçalves, A., Alemayehu, F. (eds) Probabilistic Prognostics and Health Management of Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-55852-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-55852-3_4

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