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Use of Cyclostationarity Based Condition Indicators for Gear Fault Diagnosis Under Fluctuating Speed Condition

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Cyclostationarity: Theory and Methods III

Part of the book series: Applied Condition Monitoring ((ACM,volume 6))

Abstract

The vibration based gear health monitoring is one of the condition monitoring techniques, widely used in industry. Under fluctuating speed conditions, vibration based conventional gear fault diagnosis methods like FFT and condition indicators (CI) like rms and kurtosis, fails to differentiate a faulty gear from a healthy one. Under such conditions, cyclic changes are observed in mean and variance of a vibration signal. CI using such statistical parameters for non-stationary gear vibration signal may mislead the entire fault diagnosis approach. In this chapter, CI based on cyclostationarity has been explained and used for gear fault diagnosis for fluctuating speed conditions. This chapter shows an advantage of using cyclostationarity based CI over conventional CI, for example rms and kurtosis, to diagnose fault. Result shows the effectiveness of the cyclostationarity based CI in differentiating gear health.

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References

  1. Jiang, H., Shao, Y., & Mechefske, C. K. (2014). Dynamic characteristics of helical gears under sliding friction with spalling defect. Engineering Failure Analysis, 39, 92–107.

    Article  Google Scholar 

  2. Hun, C., Smith, W. A., Randall, R. B., & Peng, Z. (2016). Development of a gear vibration indicator and its application in gear wear monitoring. Mechanical Systems and Signal Processing, 76–77, 319–336.

    Google Scholar 

  3. Li, Z., & Mao, K. (2013). Frictional effects on gear tooth contact analysis. Advances in Tribology. doi:10.1155/2013/181048.

    Google Scholar 

  4. Kidar, T., Thomas, M., Badaoui, M. El & Guilbault, R. (2013). Diagnosis of gear faults by cyclostionaty. http://surveillance7.sciencesconf.org/conference/surveillance7/27_diagnosis_of_gear_faults_by_cyclostationarity.pdf.

  5. Decker, H. J. (2002) Gear crack detection using tooth analysis‖, NASA-TM2002-211491.

    Google Scholar 

  6. Decker, H. J. & Lewicki, D. G. (2003). Spiral bevel pinion crack detection in a helicopter gearbox. In Proceedings of the American Helicopter Society 59th Annual Forum (pp. 1222–1232). Phoenix, AZ.

    Google Scholar 

  7. Zakrajsek J. J., Townsend D. P. & Decker H. J. (1993, January). An analysis of gear fault detection methods as applied to pitting fatigue failure data, Technical Report NASA TM-105950, AVSCOM TR-92-C-035, NASA and the US Army Aviation Systems Command.

    Google Scholar 

  8. Ahamed, N., Pandya, Y., & Parey, A. (2014). Spur gear tooth root crack detection using time synchronous averaging under fluctuating speed. Measurement, 52, 1–11.

    Article  Google Scholar 

  9. Sharma, V., & Parey, A. (2016). Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions. Measurement, 90, 560–575. doi:10.1016/j.measurement.2016.04.076.

    Article  Google Scholar 

  10. Pan, H. & Yuan, J. (2013). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8228 LNCS (PART 3) (pp. 425–432).

    Google Scholar 

  11. Al-Balushi, K. R., & Samanta, B. (2002). Gear fault diagnosis using energy-based features of acoustic emission signals. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 216(3), 249–263. doi:10.1177/095965180221600304.

    Google Scholar 

  12. Feng, Z., Chen, X., & Liang, M. (2016). Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under nonstationary conditions. Mechanical Systems and Signal Processing, 76–77, 242–264.

    Article  Google Scholar 

  13. Yang, H., Mathew, J., & Ma, L. (2003). Vibration feature extraction techniques for fault diagnosis of rotating machinery—a literature survey. In Asia Pacific Vibration Conference. Gold Coast, Australia.

    Google Scholar 

  14. Gelman, L., Petrunin, I., Jennions, I. K. & Walters M. (2012). Diagnostics of local tooth damage in gears by the wavelet technology. International Journal of Prognostics and Health Management. ISSN: 2153-2648, 2012 005.

    Google Scholar 

  15. Zhu, Z. K., Feng, Z. H., & Kong, F. R. (2005). Cyclostationarity analysis for gearbox condition monitoring: Approaches and effectiveness. Mechanical Systems and Signal Processing, 19, 467–482.

    Article  Google Scholar 

  16. Zhao, M., Lin, J., Wang, X., Lei, Y., & Cao, J. (2013). A tacho-less order tracking technique for large speed variations. Mechanical Systems and Signal Processing, 40, 76–90.

    Article  Google Scholar 

  17. Li, C., & Liang, M. (2013). Time–frequency signal analysis for gear box fault diagnosis using a generalized synchrosqueezing transform. Mechanical Systems and Signal Processing, 26, 205–217.

    Article  Google Scholar 

  18. Dalpiaz, G., Rivola, A., & Rubini, R. (2000). Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears. Mechanical Systems and Signal Processing, 14(3), 387–412.

    Article  Google Scholar 

  19. Antoni, J., Bonnardot, F., Raad, A., & Badaoui M. El. (2004). Cyclostationary modelling of rotating machine vibration signals. Mechanical Systems and Signal Processing, 18, 1285–1314.

    Google Scholar 

  20. Bonnardot, F., Randall, R. B., & Guillet, F. (2005). Extraction of second-order cyclostationary sources-application to vibration analysis. Mechanical Systems and Signal Processing, 19, 1230–1244.

    Article  Google Scholar 

  21. Estupinan, E., White, P., & Martin, C. S. (2007). A cyclostationary analysis applied to detection and diagnosis of faults in helicopter gearboxes. Berlin, Heidelberg: Springer.

    Google Scholar 

  22. Bartelmus, W. (2009). Vibration Diagnostic Method for Planetary Gearboxes Under Varying External Load With Regard to Cyclostationary Analysis, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław.

    Google Scholar 

  23. Zimroz, R., & Bartelmus, W. (2009). Gearbox condition estimation using cyclostationary properties of vibration signal. Key Engineering Materials, 413–414, 471–478.

    Article  Google Scholar 

  24. Fu, L., & Li, H. (2009). Gear fault diagnosis based on order tracking and degree of cyclostationarity. IEEE. doi:10.1109/CISP.2009.5301557.

    Google Scholar 

  25. Feng, Z., Zuo, M. J., Hao, R., Chu, F., & Badaoui, M. El. (2011). Gear damage assessment based on cyclic spectral analysis. IEEE Transactions on Reliability, 60(1), 21–32.

    Article  Google Scholar 

  26. Boungou, D., Guillet, F., Lyonnet, P., & Rosario, T. (2015). Fatigue damage detection using cyclostationarity. Mechanical Systems and Signal Processing, 58–59, 128–142.

    Article  Google Scholar 

  27. Abboud, D., Antoni, J., Eltabach, M., & Sieg-Zieba, S. (2015). Angle/time cyclostationarity for the analysis of rolling element bearing vibrations. Measurement, 75, 29–39.

    Article  Google Scholar 

  28. Antoni, J. (2009). Cyclostationarity by examples. Mechanical Systems and Signal Processing, 23, 987–1036.

    Article  Google Scholar 

  29. Napolitano, A. (2016). Cyclostationarity: New trends and applications. Signal Processing, 120, 85–408.

    Google Scholar 

  30. Gardner, W. (1986). Measurement of spectral correlation. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(5), 1111–1123.

    Google Scholar 

  31. Baudin, S., Rémond, D., Antoni, J., & Sauvage, O. (2016). Non-intrusive rattle noise detection in non-stationary conditions by an angle/time cyclostationary approach. Journal of Sound and Vibration, 366, 501–513.

    Article  Google Scholar 

  32. Girondin, V., Pekpe, K. M.a, Morel, H. & Cassar J. P. (2013). Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis. Mechanical Systems and Signal Processing, 38, 499–514.

    Google Scholar 

  33. Kebabsa, T., Ouelaa, N., Antoni, J., Djamaa, M. C., Khettabi, R., & Djebala, A. (2015). Experimental study of a turbo-alternator in industrial environment using cyclostationarity analysis. International Journal of Advanced Manufacturing Technology, 81, 537–552.

    Article  Google Scholar 

  34. Raad, A., Antoni, J., & Sidahmed, M. (2008). Indicators of cyclostationarity: Theory and application to gear fault monitoring. Mechanical Systems and Signal Processing, 22, 574–587.

    Article  Google Scholar 

  35. Delvecchio, S., D’Elia, G., & Dalpiaz, G. (2015). On the use of cyclostationary indicators in IC engine quality control by cold tests. Mechanical Systems and Signal Processing, 60–61, 208–228.

    Article  Google Scholar 

  36. Borghesani, P., Pennacchi, P., Ricci, R., & Chatterton, S. (2013). Testing second order cyclostationarity in the squared envelope spectrum of non-white vibration signals. Mechanical Systems and Signal Processing, 40, 38–55.

    Article  Google Scholar 

  37. Léonard, F. (2015). Time domain cyclostationarity signal-processing tools. Mechanical Systems and Signal Processing, 62–63, 100–112.

    Article  Google Scholar 

  38. Feng, Z. & Chu, F. (2015). Cyclostationary analysis for gearbox and bearing fault diagnosis. Shock and Vibration, 542472, http://dx.doi.org/10.1155/2015/542472.

  39. Eltabach, M., Sieg-Zieba, S., Vervaeke, T., Padioleau, E. & Berlingen, S. (2009). Monitoring volumetric gear pumps using cyclostationarity of the downstream pressure signals. Cetim.

    Google Scholar 

  40. Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—a tutorial. Mechanical Systems and Signal Processing, 25, 485–520.

    Article  Google Scholar 

  41. Leclere, Q., & Hamzaoui, N. (2014). Using the moving synchronous average to analyze fuzzy cyclostationary signals. Mechanical Systems and Signal Processing, 44, 149–159.

    Article  Google Scholar 

  42. Assaad, B., Eltabach, M., & Antoni, J. (2014). Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes. Mechanical Systems and Signal Processing, 42, 351–367.

    Article  Google Scholar 

  43. Jafarizadeh, M. A., Hassannejad, R., Ettefagh, M. M., & Chitsaz, S. (2008). Asynchronous input gear damage diagnosis using time averaging and wavelet filtering. Mechanical Systems and Signal Processing, 22, 172–201.

    Article  Google Scholar 

  44. Bonnardot, F., Badaoui, M. EI, Randall, R. B., Danière, J. & Guillet F. (2005). Use of the acceleration signal of the gearbox in order to perform angular Resampling (with limited speed fluctuation). Mechanical Systems and Signal Processing, 19, 766–785.

    Google Scholar 

  45. Combet, F., & Gelman, L. (2007). An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor. Mechanical Systems and Signal Processing, 21, 2590–2606.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Indore, Indore, 453552, India

    Vikas Sharma & Anand Parey

Authors
  1. Vikas Sharma
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  2. Anand Parey
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Corresponding author

Correspondence to Anand Parey .

Editor information

Editors and Affiliations

  1. National School of Engineers of Sfax , Sfax, Tunisia

    Fakher Chaari

  2. Institute of Mathematics, Cracow University of Technology, Cracow, Poland

    Jacek Leskow

  3. Department of Engineering, University of Napoli “Parthenope” , Napoli, Italy

    Antonio Napolitano

  4. Institute of Mining Engineering, Wrocław University of Technology, Wroclaw, Poland

    Radoslaw Zimroz

  5. Institute of Mathematics and computer Science, Wroclaw University of Technology, Wroclaw, Poland

    Agnieszka Wylomanska

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Sharma, V., Parey, A. (2017). Use of Cyclostationarity Based Condition Indicators for Gear Fault Diagnosis Under Fluctuating Speed Condition. In: Chaari, F., Leskow, J., Napolitano, A., Zimroz, R., Wylomanska, A. (eds) Cyclostationarity: Theory and Methods III. Applied Condition Monitoring, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-51445-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-51445-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51444-4

  • Online ISBN: 978-3-319-51445-1

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