DOI QR코드

DOI QR Code

Development of gear fault diagnosis architecture for combat aircraft engine

  • Rajdeep De (Department of Mechanical Engineering, DIAT (DU)) ;
  • S.K. Panigrahi (Department of Mechanical Engineering, DIAT (DU))
  • Received : 2022.08.04
  • Accepted : 2023.07.31
  • Published : 2023.07.25

Abstract

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

Keywords

References

  1. Abu-Mahfouz, I.A. (2005), "A comparative study of three artificial neural networks for the detection and classification of gear faults", Int. J. General Syst., 34(3), 261-277. https://doi.org/10.1080/03081070500065726.
  2. Bansal, S., Sahoo, S., Tiwari, R., and Bordoloi, D.J. (2013), "Multiclass fault diagnosis in gears using support vector machine algorithms based on frequency domain data", Measurement, 46(9), 3469-3481. https://doi.org/10.1016/j.measurement.2013.05.015.
  3. Bartelmus, W., and Zimroz, R. (2009), "Vibration condition monitoring of planetary gearbox under varying external load", Mech. Syst. Signal Pr., 23(1), 246-257. https://doi.org/10.1016/j.ymssp.2008.03.016.
  4. Bartelmus, W., and Zimroz, R. (2009), "A new feature for monitoring the condition of gearboxes in nonstationary operating conditions", Mech. Syst. Signal Pr., 23(1), 1528-1534. https://doi.org/10.1016/j.ymssp.2009.01.014.
  5. Bechhoefer, E., Qu, Y., Zhu, J., and He D. (2013), "Signal processing techniques to improve an acoustic emission sensor," Annual Conference of the Prognostics and Health Management Society, 4, 1-8. https://doi.org/10.36001/phmconf.2013.v5i1.2174.
  6. CBM Connect (2020), GMF Calculation and Gearbox Problems Detection; www.cbmconnect.com/gmfcalculationandgearboxproblemsdetection/.
  7. Chaari, F., Fakhfakh, T. and Haddar, M. (2006), "Dynamic Analysis of a planetary gear failure caused by tooth pitting and cracking", J. Fail. Anal. Prev., 6 (2),73-78. https://doi.org/10.1361/154770206X99343.
  8. Chen, H., Sun, Y., Shi, Z. and Lin, J. (2016), "Intelligent analysis method of gear faults based on FRWT and SVM", Shock Vib., 2016, 1582738. https://doi.org/10.1155/2016/1582738.
  9. Cheng, G., Cheng, Y. and Shen, L. et al. (2013), "Gear fault identification based on Hilbert-Huang transform and SOM neural network", Measurement, 46 (3), 1137-1146. https://doi.org/10.1016/j.measurement.2012.10.026.
  10. Chen, D., Wang, and W.J. (2002), "Classification of wavelet map patterns using multi-layer neural networks for gear fault detection", Mech. Syst. Signal Pr., 16(4), 695-704. https://doi.org/10.1006/mssp.2002.1488.
  11. Chen X, Hong J, Wang Y, and Ma Y (2021), "Fatigue failure analysis of the central-driven bevel gear in a turboshaft engine arising from multi-source excitation", Eng. Fail. Anal., 119, 104811. https://doi.org/10.1016/j.engfailanal.2020.104811.
  12. Cirrincione, G., Kumar,R.R, Mohammadi,, A., Kia, S.H., Barbiero, P. and Ferrett, J. (2020), "Shallow versus deep neural networks in gear fault diagnosis", IEEE T. Energy Convers., 35(3), 1338-1347. https://doi.org/10.1109/TEC.2020.2978155.
  13. Cular I, Vuckovic K, Galic I, and Zezelj D (2023), "Computational model for bending fatigue life and failure location prediction of surface-hardened running gears", Int. J. Fatigue, 166, 107300. https://doi.org/10.1016/j.ijfatigue.2022.107300.
  14. Decker, H.J. (2002), "Crack detection for aerospace quality spur gears," NASA TM-2002-211492, ARL-TR-2682, NASA and the US Army Research Laboratory.
  15. Decker, H.J. and Lewicki, D.G. (2003), "Spiral bevel pinion crack detection in a helicopter gearbox, Proceedings of the American Helicopter Society", 59th Annual Forum, 1222-1232, Phoenix, Arizona.
  16. Dempsey, P.J. and Zakrajsek, J.J. (2001), "Minimizing load effects on NA4 gear vibration diagnostic parameter", Proceedings of the 55th Meeting sponsored by the Society for Machine Failure Prevention Technology, Virginia Beach, Virginia, April.
  17. Fernandez, A. (2022), Frequencies of a gear assembly; power-mi.com/content/frequencies-gear-assembly.
  18. Feng, Z.P., Liang, M., Zhang, Y. and Zhang, Y. (2012), "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation", Renew. Energy, 47, 112-126. https://doi.org/10.1016/j.renene.2012.04.019.
  19. Figshare Dataset (2018), Gear Fault Data; figshare.com/articles/dataset/Gear_Fault_Data/6127874/1.
  20. GearConditionMetrics (2019), Standard Metrics for Gear Condition Monitoring; www.mathworks.com/help/predmaint/ref/gearconditionmetrics.html.
  21. Heydari, A. (2021), "The damped vibration of the annular and rectangular graded beams in the presence of the attached lumped mass", Adv. Comput. Des., 6(4), 329-338. https://doi.org/10.12989/acd.2021.6.4.329.
  22. Hines, J.A., Muench, D.S. and Keller, J.A. (2005), "Effects of time-synchronous averaging implementations on HUMS features for UH-60A planetary carrier cracking", Annual Forum Proceedings-American Helicopter Society, Grapevine, Texas, U.S.A., June.
  23. Huangfu, Y., Zhao, Z., Ma, H., Han, H. and Chen, K. (2020), "Effects of tooth modifications on the dynamic characteristics of thin-rimmed gears under surface wear", Mech. Mach. Theory, 150, 103870. https://doi.org/10.1016/j.mechmachtheory.2020.103870.
  24. James, C.L. and Limmer, J.D. (2000), "Model-based condition index for tracking gear wear and fatigue damage", Wear, 241(1), 26-32. https://doi.org/10.1016/S0043-1648(00)00356-2.
  25. Jiang,,Y.H., Tang, B.P. and Qin, Y. et al. (2011), "Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD", Renewable Energy, 36(8), 2146-2153. https://doi.org/10.1016/j.renene.2011.01.009.
  26. Jing, L., Zhao, M., Li, P. and Xu, X. (2017), "A convolutional neural network-based feature learning and fault diagnosis method for the condition monitoring of gearbox", Measurement, 111, 1-10. https://doi.org/10.1016/j.measurement.2017.07.017.
  27. Keller, J.A. and Grabill, P. (2003), "Vibration monitoring of UH-60A main transmission planetary carrier fault", American Helicopter Society 59th Annual Forum, Phoenix, Arizona, U.S.A., May.
  28. Li, W., Zhang, L. and Xu, Y. (2012), "Gearbox pitting detection using linear discriminant analysis and distance preserving self-organizing map", IEEE International Instrumentation and Measurement Technology Conference Proceedings, 2225-2229. https://doi.org/10.1109/I2MTC.2012.6229667.
  29. Liu, W.Y., Zhang, W.H., Han, J.G. and Wang, G.F. (2012), "A new wind turbine fault diagnosis method based on the local mean decomposition", Renewable Energy, 48, 411-415. https://doi.org/10.1016/j.renene.2012.05.018.
  30. Liu, X., Yang, Y. and Zhang, J. (2018), "Resultant vibration signal model-based fault diagnosis of a single-stage planetary gear train with an incipient tooth crack on the sun gear", Renewable Energy, 122, 65-79. https://doi.org/10.1016/j.renene.2018.01.072.
  31. Luo, Y., Cui, L., Zhang, J. and Ma, J. (2021), "Vibration mechanism and improved phenomenological model of planetary gearbox with broken sun gear fault", Measurement, 178, 109356. https://doi.org/10.1016/j.measurement.2021.109356.
  32. Ma, H., Pang, X., Feng, R., Song, R. and Wen, B. (2015), "Fault features analysis of cracked gear considering the effects of the extended tooth contact", Eng. Fail. Anal., 48, 105-120. https://doi.org/10.1016/j.engfailanal.2014.11.018.
  33. Machinery Lubrication (2022), How to Analyze Gear Failures: Noria corporation, Tusla, U.S.A. www.machinerylubrication.com/Read/150/.
  34. Mark, W.D., Lee, and Patrick, H.R. and Coker, J.D. (2010), "A simple frequency-domain algorithm for early detection of damaged gear teeth", Mech. Syst. Signal Pr., 24, 2807-2823. https://doi.org/10.1016/j.ymssp.2010.04.004.
  35. McFadden, P.D. (1991), "A technique for calculation the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox", J. Sound Vib., 144(l), 163-172. https://doi.org/10.1016/0022-460X(91)90739-7.
  36. McFadden, P.D. and Howard, I.M. (1990), "The detection of seeded faults in an epicyclic gearbox by signal averaging of the vibration", Technical Report, Propulsion Report 183, Aeronautical Research Laboratory, Melbourne, Australia.
  37. McNames, J. (2001), "Fourier series analysis of epicyclic gearbox vibration", J. Vib. Acoust., 124, 150-153. https://doi.org/10.1115/1.1403735.
  38. Prabhu R, and Devaraju A. (2021), "Failure analysis and restructuring model of transfer feeder gear box in thermal powerplant", Mater. Today Proc., 39, 633-638. https://doi.org/10.1016/j.matpr.2020.08.808.
  39. Qiang, Z., Jieying, G., Junming, L., Ying, T. and Shilei, Z. (2020), "Gearbox fault diagnosis using data fusion based on self-organizing map neural network", Int. J. Distrib. Sens. N., 16(5), 1-11. https://doi.org/10.1177/1550147720923476.
  40. Rolls-Royce (2015), The Jet Engine, 5th edition, Wiley, New York, U.S.A. 
  41. Sanz, J., Perera, and Huerta, R.C. (2012), "Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks", Appl. Soft Comput., 12(9), 2867-2878. https://doi.org/10.1016/j.asoc.2012.04.003.
  42. Saravanan, N., Siddabattuni, V.N.S.K. and Ramachandran, K.I. (2010), "Fault diagnosis of spur bevel gear box using artificial neural network (ANN) and proximal support vector machine (PSVM)", Appl. Soft Comput., 10(1), 344-360. https://doi.org/10.1016/j.asoc.2009.08.006.
  43. Saxena, A., Wu, B.Q. and Vachtsevanos, G. (2005), "A methodology for analyzing vibration data from planetary gear systems using complex Morlet wavelets", Proceedings of the 2005, American Control Conference, 4730-4735, Portland, Oregon, U.S.A., June. https://doi.org/10.1109/ACC.2005.1470743.
  44. Schon, P.P. (2005), "Unconditionally convergent time domain adaptive and time-frequency techniques for epicyclic gearbox vibration", Mechanical and Aeronautical Engineering, Master Thesis, University of Pretoria, Pretoria, South Africa.
  45. Shen, C.H., Wen, J., Arunyanart, P. and Choy, F.K. (2011), "Vibration signature analysis and parameter extractions on damages in gears and rolling element bearings", ISRN Mech. Eng., 2011, 402928. https://doi.org/10.5402/2011/40292.
  46. Singleton, K. (2006), "Case Study-Analysis of two-stage planetary gearbox vibration", KSC Consulting LLC, Case Study.
  47. Smidt, M.R. (2010), "Internal vibration monitoring of a planetary gearbox", Master Thesis, University of Pretoria, Pretoria, South Africa.
  48. Sparis, G.P. and Vachtsevanos, N.D (2022), "Automatic diagnostic feature generation via the fast fourier transform", Technical Report.
  49. Sunder, S.T. and Hemalatha, S. (2021), "Experimental analysis of mechanical vibration in 225 kW wind turbine gear box", Mater. Today Pr., 46, 3292-3296. https://doi.org/10.1016/j.matpr.2020.11.461.
  50. Vecer, P., Kriedl M. and Smĭd R. (2005), "Condition Indicators for gearbox condition monitoring systems", Acta Polytechnica, 45(6), 35-43.
  51. Wang, W. (2001), "Early detection of gear tooth cracking using the resonance demodulation technique", Mech. Syst. Signal Pr., 15, 887-903. https://doi.org/10.1006/mssp.2001.1416.
  52. Wang, X. and Makis, V. (2009), "Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test," J. Sound Vib., 327(3-5),413-423. https://doi.org/10.1016/j.jsv.2009.07.004.
  53. Wu, B.Q., Saxena A, and Patrick R, Vachtsevanos, G. (2005), "Vibration monitoring for fault diagnosis of helicopter planetary gears", IFAC Proceedings Volumes, 38(1), 755-60. https://doi.org/10.3182/20050703-6-CZ-1902.00127.
  54. Wu, S., Zuo, M.J. and Parey, A. (2008), "Simulation of spur gear dynamics and estimation of fault growth", J. Sound Vib., 317, 608-624. https://doi.org/10.1016/j.jsv.2008.03.038.
  55. Xing, Z., Qu, J., Chai, Y., Tang, Q. and Zhou, Y. (2017), "Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine", J. Mech. Sci. Technol., 31(2), 545-553. https://doi.org/10.1007/s12206-017-0107-3.
  56. Yang, X., Ding, K. and He, G. (2019), "Phenomenon-model-based AM-FM vibration mechanism of faulty spur gear", Mech. Syst. Signal Pr., 134, 106366. https://doi.org/10.1016/j.ymssp.2019.106366.
  57. Yip, L. (2011), "Analysis and modelling of planetary gearbox vibration data for early fault detection", Master Thesis, University of Toronto, Toronto, Canada 2011. https://hdl.handle.net/1807/31653.
  58. Yu, J. (2011), "Early fault detection for gear shaft and planetary gear based on wavelet and hidden Markov modeling", Doctoral Thesis, University of Toronto, Toronto, Canada.
  59. Li, Y.X., Jiao, S.B., Geng, B., Zhang, Q. and Zhang, Y.M. (2022), "A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise", J. Defense Technol., 18(2), 183-193. https://doi.org/10.1016/j.dt.2020.11.011.
  60. Li, Y.X., Jiao, S.B. and Gao, X. (2021), "A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy", J. Defense Technol., 17(5), 1625-1635. https://doi.org/10.1016/j.dt.2020.09.001.
  61. Zakrajsek, J.J. and Lewicki, D.G. (1996), "Detecting gear tooth fatigue cracks in advance of complete fracture", NASA TM-107145, ARL TR-970, NASA and the US Army Aviation Systems Command, April.
  62. Zakrajsek, J.J., Townsend, D.P. and Decker, H.J. (1993), "An analysis of gear fault detection methods as applied to pitting fatigue failure data", NASA TM-105950, AVSCOM TR-92-C-035, NASA and the US Army Aviation Systems Command, January.
  63. Zhang, X.H., Kang J.S., Zhao, J.S. and Cao, D.C. (2013), "Features for fault diagnosis and prognosis of gearbox", Chem. Eng. Transact., 33, 1027-1032.