Smart Structures and Systems

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Application of compressive sensing and variance considered machine to condition monitoring

  • Lee, Myung Jun (School of Mechanical Engineering, Chonnam National University) ;
  • Jun, Jun Young (School of Mechanical Engineering, Chonnam National University) ;
  • Park, Gyuhae (School of Mechanical Engineering, Chonnam National University) ;
  • Kang, To (Nuclear Convergence Technology Division, Korea Atomic Energy Research Institute) ;
  • Han, Soon Woo (Nuclear Convergence Technology Division, Korea Atomic Energy Research Institute)
  • Received : 2017.05.20
  • Accepted : 2017.11.28
  • Published : 2018.08.25
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Abstract

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

References

  1. Carden, E.P. and Fanning, P., (2004), "Vibration based condition monitoring: A review", Struct. Health Monit., 3(4), 355-377 https://doi.org/10.1177/1475921704047500
  2. Donoho, D.L. (2006), "Compressed sensing. Information theory", IEEE T. Inform. Theory, 52(4), 1289-1306. https://doi.org/10.1109/TIT.2006.871582
  3. Hakim, S.J.S. and Abdul Razak, H. (2014), "Modal parameters based structural damage detection using artificial neural networks - a review", Smart Struct. Syst., 14(2), 159-189. https://doi.org/10.12989/sss.2014.14.2.159
  4. Hoglund, J., Voigt, T., Wei, B., Hu, W. and Karoumi, R. (2014), "Compressive Sensing for Bridge Damage Detection", Proceedings of 5th Nordic Workshop on System and Network Optimization for Wireless, Are, Sweden, April
  5. Jardine, A.K.S., Lin, D. and Banjevic, D. (2006), "A review on machinery diagnostics and prognostics implementing conditionbased maintenance", Mech. Syst. Signal Pr., 20(7), 1483-1510 https://doi.org/10.1016/j.ymssp.2005.09.012
  6. Jayawardhana, M., Zhu, X., Liyanapathirana, L. and Gunawardana, U. (2017), "Compressive sensing for efficient health monitoring and effective damage detection of structures", Mech. Syst. Signal Pr., 84, 414-430. https://doi.org/10.1016/j.ymssp.2016.07.027
  7. Jung, U.K. 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. Lei, Y., Lin, J., Zuo, J.J. and He, Z. (2014), "Condition monitoring and fault diagnosis of planetary gearboxes: A review", Measurements, 48, 292-305
  9. Loutas, T.H., Sotiriades, G., Kalaitzoglou, I. and Kostopoulos, V. (2009) "Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements", Appl. Acoust., 70(9), 1148-1159. https://doi.org/10.1016/j.apacoust.2009.04.007
  10. Mascarenas, D., Cattaneo, A., Theiler, J. and Farrar, C.R. (2013), "Compressed sensing techniques for detecting damage in structures", Struct. Health Monit., 12(4) 325-338
  11. Needell, D. and Tropp, J.A. (2009), "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples", Appl. Comput. Harmon. A., 26 (3), 301-321. https://doi.org/10.1016/j.acha.2008.07.002
  12. Park, J.Y., Wakin, M.B. and Gilbert, A.C. (2014) "Modal analysis with compressive measurements", IEEE T. Signal Proces., 62(7), 1655-1670. https://doi.org/10.1109/TSP.2014.2302736
  13. Peng, Z.K. and Chu, F.L. (2004), "Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography", Mech. Syst. Signal Pr., 18(2), 199-221. https://doi.org/10.1016/S0888-3270(03)00075-X
  14. Samuel, P.D. and Pines, D.J. (2005), "A review of vibration-based techniques for helicopter transmission diagnostics", J. Sound Vib., 282(1-2) 475-508 https://doi.org/10.1016/j.jsv.2004.02.058
  15. Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T.A. and Ekemb, G. (2014), "Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges", Energies, 7(4), 2595-2630 https://doi.org/10.3390/en7042595
  16. Tropp J.A. and Gilbert, A.C. (2007), "Signal recovery from random measurements via orthogonal matching pursuit", IEEE T. Inform. Theory, 53 (12), 4655-4666. https://doi.org/10.1109/TIT.2007.909108
  17. Tropp, J.A. (2004), "Greed is good: algorithmic results for sparse approximation", IEEE T. Inform. Theory, 50 2231-2242. https://doi.org/10.1109/TIT.2004.834793
  18. Yeom, H., Jang, I. and Sim, K. (2009), "Variance considered machines: modification of optimal hyperplanes in support vector machines", Proceedings of the 2009 IEEE International Symposium on Industrial Electronics. IEEE.