Smart Structures and Systems

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Modification of acceleration signal to improve classification performance of valve defects in a linear compressor

  • Kim, Yeon-Woo (Department of Mechanical Engineering, Pusan National University) ;
  • Jeong, Wei-Bong (Department of Mechanical Engineering, Pusan National University)
  • Received : 2018.07.17
  • Accepted : 2019.01.15
  • Published : 2019.01.25
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Abstract

In general, it may be advantageous to measure the pressure pulsation near a valve to detect a valve defect in a linear compressor. However, the acceleration signals are more advantageous for rapid classification in a mass-production line. This paper deals with the performance improvement of fault classification using only the compressor-shell acceleration signal based on the relation between the refrigerant pressure pulsation and the shell acceleration of the compressor. A transfer function was estimated experimentally to take into account the signal noise ratio between the pressure pulsation of the refrigerant in the suction pipe and the shell acceleration. The shell acceleration signal of the compressor was modified using this transfer function to improve the defect classification performance. The defect classification of the modified signal was evaluated in the acceleration signal in the frequency domain using Fisher's discriminant ratio (FDR). The defect classification method was validated by experimental data. By using the method presented, the classification of valve defects can be performed rapidly and efficiently during mass production.

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References

  1. AlThobiani, F. and Ball, A. (2014), "An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks", Expert Syst. Appl., 41(9), 4113-4122. https://doi.org/10.1016/j.eswa.2013.12.026
  2. Aretakis, N. and Mathioudakis, K. (1998), "Classification of radial compressor faults using pattern-recognition techniques", Control Eng.Pract., 6(10), 1217-1223. https://doi.org/10.1016/S0967-0661(98)00085-9
  3. Attoui, I., Fergani, N., Boutasseta, N., Oudjani, B., and Deliou, A. (2017), "A new time-frequency method for identification and classification of ball bearing faults", J. Sound Vib., 397, 241-265. https://doi.org/10.1016/j.jsv.2017.02.041
  4. Cui, H., Zhang, L., Kang, R. and Lan, X. (2009), "Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method", J. Loss Prevent. Proc., 22(6), 864-867. https://doi.org/10.1016/j.jlp.2009.08.012
  5. Elhaj, M., Gu, F., Ball, A.D., Albarbar, A., Al-Qattan, M. and Naid, A. (2008), "Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring", Mech. Syst. Signal Pr., 22(2), 374-389. https://doi.org/10.1016/j.ymssp.2007.08.003
  6. Fan, Y., Nowaczyk, S. and Rognvaldsson, T. (2015), "Evaluation of self-organized approach for predicting compressor faults in a city bus fleet", Procedia Comput. Sci., 53, 447-456. https://doi.org/10.1016/j.procs.2015.07.322
  7. Jung, U. and Koh, B.H. (2014), "Bearing fault detection through multiscale wavelet scalogram-based SPC", Smart Struct. Syst., 14(3), 377-395. https://doi.org/10.12989/sss.2014.14.3.377
  8. Kim, M. and Kim, M. S. (2005), "Performance investigation of a variable speed vapor compression system for fault detection and diagnosis", Int. J. Refrig., 28(4), 481-488. https://doi.org/10.1016/j.ijrefrig.2004.11.008
  9. Lei, Y., He, Z. and Zi, Y. (2008), "A new approach to intelligent fault diagnosis of rotating machinery", Expert Syst. Appl., 35(4), 1593-1600. https://doi.org/10.1016/j.eswa.2007.08.072
  10. Mathioudakis, K. and Stamatis, A. (1994), "Compressor fault identification from overall performance data based on adaptive stage stacking", J. Eng. Gas Turb. Power., 116(1), 156-164. https://doi.org/10.1115/1.2906785
  11. Pichler, K., Lughofer, E., Pichler, M., Buchegger, T., Klement, E. P. and Huschenbett, M. (2016), "Fault detection in reciprocating compressor valves under varying load conditions", Mech. Syst. Signal Pr., 70, 104-119. https://doi.org/10.1016/j.ymssp.2015.09.005
  12. Qi, G., Tsai, W.T., Hong, Y., Wang, W., Hou, G. and Zhu, Z. (2016), "Fault-diagnosis for reciprocating compressors using big data", Proceedings of the 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications., 72-81.
  13. Shen, C., Wang, D., Liu, Y., Kong, F. and Tse, P.W. (2014), "Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines", Smart Struct. Syst., 13(3), 453-471. https://doi.org/10.12989/sss.2014.13.3.453
  14. Shin, K. and Hammond, J. (2008), Fundamentals of signal processing for sound and vibration engineers, John Wiley & Sons.
  15. Tassou, S. A., and Grace, I. N. (2005), "Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems", Int. J. Refrigeration, 28(5), 680-688. https://doi.org/10.1016/j.ijrefrig.2004.12.007
  16. Tran, V.T., AlThobiani, F., Tinga, T., Ball, A. and Niu, G. (2017), "Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network", Proceedings of the Institution of Mechanical Engineers., Part C: J. Mech. Eng. Sci., 0954406217740929.
  17. Wang, Y., Xue, C., Jia, X. and Peng, X. (2015), "Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion", Mech. Syst. Signal Pr., 56, 197-212. https://doi.org/10.1016/j.ymssp.2014.11.002
  18. White, P.R., Tan, M.H. and Hammond, J.K. (2006), "Analysis of the maximum likelihood, total least squares and principal component approaches for frequency response function estimation", J. Sound Vib., 290(3-5), 676-689. https://doi.org/10.1016/j.jsv.2005.04.029
  19. Yang, B. S., Hwang, W. W., Kim, D. J., and Tan, A. C. (2005), "Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines", Mech. Syst. Signal Pr., 19(2), 371-390. https://doi.org/10.1016/j.ymssp.2004.06.002
  20. Zhou, Y., MA, X. and Yuan, Y. (2006), "Reciprocating compressor fault diagnosis based on vibration signal information entropy", Machine Tool & Hydraulics., 10, 069.
  21. Zhu, D., Feng, Y., Chen, Q. and Cai, J. (2010), "Image recognition technology in rotating machinery fault diagnosis based on artificial immune", Smart Struct. Syst., 6(4), 389-403. https://doi.org/10.12989/sss.2010.6.4.389