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Neural network model for condition monitoring of wear and film thickness in a gearbox

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Abstract

Mechanical gears are used to transmit power and motion in mechanical, electrical and chemical process industries. Influenced by vibration, torque, temperature, lubrication and specific film thickness, the gear teeth contacts may experience change leading to unexpected failures such as wear, scuffing, pitting and micro-pitting on teeth surface. In order to avoid these damages, continuous monitoring is essential using knowledge-based systems. Generic capability of artificial neural network is exploited to formulate prediction and classification based on heuristic models of condition of lubricating oil in spur gears. Based on the loading conditions such as vibration, temperature and torque, the algorithm predicts film thickness to classify oil conditions as elasto-hydrodynamic, mixed wear and severe wear that helps in finding faults during operation of gears.

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References

  1. Radovich JL (1984) Gears, handbook of lubrication (theory and practice of tribology), Vol. II. In: Booser ER (ed) CRC Press. FL, Boca Raton, pp 539–564

    Google Scholar 

  2. Dudley DW (1980) Gear wear. In: Peterson MB, Winer WO (eds) Wear control handbook. The American Society of Mechanical Engineers, New York, pp 755–830

    Google Scholar 

  3. Kubo A, Townsend DP (1991) Gear lubrication, in Dudley’s Gear Handbook, In: Townsend DP (Ed), McGraw-Hill, New York, 15.1–31

  4. Samanta B, Al- Baulshi KR, Al-Araimi SA (2003) Artificial neural network based Fault diagnostics of rolling element bearings using time domain features. Mech Syst Signal Process 17(2):317–328

    Article  Google Scholar 

  5. Samantha B (2004) Gear fault using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Signal Process 18:625–644

    Article  Google Scholar 

  6. Davis A (1997) Handbook of condition monitoring. London: Chapman & Hall; 1988 In: Drosjack MJ, Houser DR (eds) An experimental and theoretical study of the effect of simulated pitch line pitting on the vibration of a geared system, ASME publication report 7-DET-123

  7. Sung CK, Tai HM, Chen HM (2000) Locating defects of gear system by the technique of wavelet transform, Mech Mach Theory

  8. Amarnath M, Sujatha C, Swarnamani S (2009) Experimental studies on the effects of reduction in gear tooth stiffness and lubricant film thickness in a spur geared system. Tribol Int 42:340–352

    Article  Google Scholar 

  9. Dowson D, Higginson GR (1977) Elastohydrodynamic lubrication. Pergamon Press Ltd, New York

    Google Scholar 

  10. Bartz JW (1993) Lubrication of gearing. Mechanical Engineering publication Ltd, London

    Google Scholar 

  11. Höhn B-R, Michaelis K (2003) Influence of oil temperature on gear failures. Tribol Int 37:103–109

    Article  Google Scholar 

  12. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing, New York

    MATH  Google Scholar 

  13. Booth C, McDonald JR (1998) The use of artificial neural networks for condition monitoring of electrical power transformers, Elsevier Science B.V. Neurocomputing 23:97–109

    Article  Google Scholar 

  14. Peng Z, Kessissoglou N (2002) An integrated approach to fault diagnosis of machinery wear debris and vibration analysis. Wear 225(7–12):1221–1232

    Google Scholar 

  15. Dempsey PJ (2002) Wear damage detection using oil debris analysis. NASA TM 211492, ARL-TR-2682

  16. Hamrock BJ, Dawson D (1977) Isothermal elastohydrodynamic lubrication of point contacts, Part III—fully flooded results. ASME J Lubr Tech 99(2):264–276

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Chemical Engineering, CSIR-CLRI, Adyar, Chennai, 600 020, India

    C. Sreepradha, Rames C. Panda, K. Harshabardhan & M. Aribalagan

  2. Department of Mechanical Engineering, College of Engineering, Anna University, Guindy, Chennai, 600 025, TamilNadu, India

    A. Krishna Kumari & A. Elaya Perumal

Authors
  1. C. Sreepradha
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  2. A. Krishna Kumari
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  3. A. Elaya Perumal
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  4. Rames C. Panda
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  5. K. Harshabardhan
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  6. M. Aribalagan
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Correspondence to Rames C. Panda.

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Sreepradha, C., Krishna Kumari, A., Elaya Perumal, A. et al. Neural network model for condition monitoring of wear and film thickness in a gearbox. Neural Comput & Applic 24, 1943–1952 (2014). https://doi.org/10.1007/s00521-013-1427-6

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  • DOI: https://doi.org/10.1007/s00521-013-1427-6

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