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A Critical Overview of the “Filterbank-Feature-Decision” Methodology in Machine Condition Monitoring

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Abstract

The number of research papers dealing with vibration-based condition monitoring has been exponentially growing in recent decades. As a consequence, one may identify some trends that emerge from this vast literature. The present paper delineates a methodology that can be recognized in several research works, which is rooted in a succession of three stages. The first stage embodies a linear transform of the data, typically in the form of a filterbank, the second stage reduces the dimension of the data through a nonlinear functional, typically in the form of health indicators, and the last stage supplies a statistical decision. Although several variants of this methodology exist, its fundamental principles seem to have converged to a general consensus, at least implicitly. This paper provides a critical overview of this methodology. It discusses its working assumptions under some typical scenarios and formulates several caveats. It also provides a few prospects that may nourish future research.

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References

  1. Rifkin, J.: The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World, Palgrave Macmillan (2011), ISBN-13 : 978–0230115217

  2. Mechanical Systems and Signal Processing, Elsevier, ISSN: 0888–3270

  3. Renewable Energy, Elsevier, ISSN: 0960–1481

  4. IEEE Transactions on Instrumentation and Measurement, IEEE Instrumentation and Measurement Society, ISSN: 0018–9456

  5. Jablonski, A.: Condition Monitoring Algorithms in MATLAB®, Springer International Publishing (2021)

  6. Schwab, K.: The fourth industrial revolution—What it means and how to respond, foreign affairs (2015)

  7. Mitchell, J.S.: Introduction to Machinery Analysis and Monitoring. PennWell Books, Tulsa (1981)

    Google Scholar 

  8. Thearle, E.L.: Dynamic balancing of rotating machinery in the field. Trans. Am. Soc. Mech. Eng. J. Appl. Mech. 56, 745–753 (1934)

    Google Scholar 

  9. Foiles, W., Allaire, P., Gunter, E.: Review: rotor balancing. Shock. Vib. 5, 325–336 (1998)

    Article  Google Scholar 

  10. Ozguven, H., Houser, D.: Mathematical-models used in gear dynamics—a review. J Sound Vib 121(3), 383–411 (1988)

    Article  Google Scholar 

  11. Liang, X., Zuo, M., Feng, Z.: Dynamic modeling of gearbox faults: a review. Mech. Syst. Signal Process 98, 852–876 (2018)

    Article  Google Scholar 

  12. Cooley, C., Parker, R.A.: Review of planetary and epicyclic gear dynamics and vibrations research. Appl. Mech. Rev. 66, 040804 (2014)

    Article  Google Scholar 

  13. McFadden, P.D., Smith, J.D.: Model for the vibration produced by a single point defect in a rolling element bearing. J. Sound Vib. 96(1), 69–82 (1984)

    Article  Google Scholar 

  14. McFadden, P.D., Smith, J.D.: Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol. Int. 17(1), 3–10 (1984)

    Article  Google Scholar 

  15. Braun, S.: Mechanical Signature, Analysis Theory and Applications. Academic Press, New York (1986)

    Google Scholar 

  16. Randall, R.B., Antoni, J.: Rolling element bearing diagnostics. Mech Syst Signal Process 25, 485–520 (2011)

    Article  Google Scholar 

  17. Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer-Verlag, London (2001)

    Book  MATH  Google Scholar 

  18. Worden, K., Staszewski, W., Hensman, J.: Natural computing for mechanical systems research: a tutorial overview. Mech. Syst. Signal Process. 25(1), 4–111 (2011)

    Article  Google Scholar 

  19. Ran, Y., Zhou, X., Lin, P., Wen, Y., Deng, R.: A survey of predictive maintenance: systems, purposes and approaches, eprint arXiv:1912.07383 (2019)

  20. Wei, Y., Li, Y., Xu, M., Huang, W.: A review of early fault diagnosis approaches and their applications in rotating machinery. Entropy 21, 409 (2019)

    Article  Google Scholar 

  21. Randall, R.B.: Vibration–based Condition Monitoring: Industrial, Aerospace and Automotive Applications, 2nd edn. Wiley-Blackwell, Chichester (2021)

    Book  Google Scholar 

  22. Barszcz, T.: Vibration-Based Condition Monitoring of Wind Turbines. Springer, Switzerland (2019)

    Book  Google Scholar 

  23. Isermann, R.: Fault-Diagnosis Systems, An Introduction from Fault Detection to Fault Tolerance. Springer-Verlag, Berlin Heidelberg (2006)

    Google Scholar 

  24. Ramsay, J.O., Silverman, B.W.: Functional Data Analysis. Springer, New York (2005)

    Book  MATH  Google Scholar 

  25. Randall, R.B.: Frequency Analysis. Bruel and Kjaer, Naerum (1987)

    Google Scholar 

  26. Goldman, S.: Vibration Spectrum Analysis. Industrial Press Inc., New York (1999)

    Google Scholar 

  27. Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process. 18, 199–221 (2004)

    Article  Google Scholar 

  28. Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Process. 96, 1–15 (2014)

    Article  Google Scholar 

  29. Tabrizi, A., Garibaldi, L., Fasana, A., Marchesiello, S.: Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine. Meccanica 50, 865–874 (2015)

    Article  Google Scholar 

  30. McFadden, P.D., Toozhy, M.M.: Application of synchronous averaging to vibration monitoring of rolling element bearing. Mech. Syst. Signal Process. 14(6), 891–906 (2000)

    Article  Google Scholar 

  31. Bechhoefer E., Kingsley M.: A review of time synchronous average algorithms, annual conference of the prognostics and health management society (2009)

  32. Braun, S.: The synchronous (time domain) average revisited. Mech. Syst. Signal Process. 25, 1087–1102 (2011)

    Article  Google Scholar 

  33. Kilundu, B., Chiementin, X., Dehombreux, P.: Singular spectrum analysis for bearing defect detection. J. Vibrat. Acoust. 133(5) (2011)

  34. Li. L., Cui Y., Chen R., Liu X.: Optimal SES selection based on SVD and its application to incipient bearing fault diagnosis, shock and vibration, p. 8067416, (2018)

  35. Zhang, D., Lv, Y., Li, Y., Wei, G.: Improved dynamic mode decomposition and its application to fault diagnosis of rolling bearing. Sensors 18(6), 1972 (2018)

    Article  Google Scholar 

  36. Cheng, C., Jia, D., Yong, Z.: A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series. Neurocomputing 406, 204–214 (2020)

    Article  Google Scholar 

  37. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  38. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62, 531–544 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  39. Feldman, M.: Hilbert transform in vibration analysis. Mech. Syst. Signal Process. 25, 735–802 (2011)

    Article  Google Scholar 

  40. Lei, Y., Lin, J., He, Z., Zuo, M.J.: A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 35, 108–126 (2013)

    Article  Google Scholar 

  41. Feng, Z., Zhang, D., Zuo, M.J.: Adaptive mode decomposition methods and their applications in signal analysis for machinery fault diagnosis: a review with examples. IEEE Access 5, 24301–24331 (2017)

    Article  Google Scholar 

  42. Chegini, S.N., Manjili, M.J.H., Bagheri, A.: New fault diagnosis approaches for detecting the bearing slight degradation. Meccanica 55, 261–286 (2020)

    Article  MathSciNet  Google Scholar 

  43. Yang, B., Liu, R., Chen, X.: Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans. Industr. Inf. 13(3), 1321–1331 (2017)

    Article  Google Scholar 

  44. Lu, Y., Xie, R., Liang, S.Y.: Adaptive online dictionary learning for bearing fault diagnosis. Int. J. Adv. Manuf. Technol. 101(1–4), 195–202 (2019)

    Article  Google Scholar 

  45. Vecer P., Kreidl M., Smid, R.: Condition indicators for gearbox condition monitoring systems, Czech Technical University in Prague, Acta Polytechnica, 45(6) (2005)

  46. Junda Z. et al.: Survey of condition indicators for condition monitoring systems.In Proceedings of the Annual Conference On The Prognostics And Health Management Society p. 5 (2014)

  47. Vikas, S., Anand, P.: A review of gear fault diagnosis using various condition indicators. Proc. Eng. 144, 253–263 (2016)

    Article  Google Scholar 

  48. Raad, A., Antoni, J., Sidahmed, M.: Indicators of cyclostationarity: Theory and application to gear fault monitoring. Mech. Syst. Signal Process. 22, 574–587 (2008)

    Article  Google Scholar 

  49. Klein, R.: Condition indicators for gears. Proceedings of the Annual Conference of the Prognostics and Health Management Society, pp. 183–190 (2012)

  50. Delvecchio, S., D’Elia, G., Dalpiaz, G.: On the use of cyclostationary indicators in IC engine quality control by cold tests. Mech. Syst. Signal Process. 60–61, 208–228 (2015)

    Article  Google Scholar 

  51. Dadon, I., Koren, N., Klein, R., Bortman, J.: A step toward fault type and severity characterization in spur gears. J. Mech. Des. p.141 (2019)

  52. Borghesani, P., Pennacchi, P., Chatterton, S.: The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings. Mech. Syst. Signal Process. 43(1–2), 25–43 (2014)

    Article  Google Scholar 

  53. Kass, S., Raad, A., Antoni, J.: Self-running bearing diagnosis based on scalar indicator using fast order frequency spectral coherence. Measurement 138, 467–484 (2019)

    Article  Google Scholar 

  54. Wang, D., Peng, Z., Xi, L.: The sum of weighted normalized square envelope: A unified framework for kurtosis, negative entropy, Gini index and smoothness index for machine health monitoring. Mech. Syst. Signal Process. 140, 106725 (2020)

    Article  Google Scholar 

  55. Borghesani, P., Antoni, J.: CS2 analysis in presence of non-Gaussian background noise - Effect on traditional estimators and resilience of log-envelope indicators. Mech. Syst. Signal Process. 90, 378–398 (2017)

    Article  Google Scholar 

  56. Wang, D.: Spectral L2/L1 norm: a new perspective for spectral kurtosis for characterizing non-stationary signals. Mech. Syst. Signal Process 104, 290–293 (2018)

    Article  Google Scholar 

  57. Bechhoefer, E., He, D., Dempsey, P.: Gear health threshold setting based on a probability of false alarm. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, pp. 275–281 (2014)

  58. Jablonski, A., Bielecka, M., Barszcz, T.: Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems. Measurement 46(1), 727–738 (2013)

    Article  Google Scholar 

  59. Liu, R., Yang, B., Zio, E., Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process 108, 33–47 (2018)

    Article  Google Scholar 

  60. Smith, W.A., Borghesani, P., Ni, Q., Wang, K., Peng, Z.: Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools. Mech. Syst. Signal Process. 134, 106303 (2019)

    Article  Google Scholar 

  61. Antoni, J.: Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21(1), 108–124 (2007)

    Article  Google Scholar 

  62. Wang, Y., Xiang, J., Markert, R., Liang, M.: Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications. Mech. Syst. Signal Process. 66–67, 679–698 (2016)

    Article  Google Scholar 

  63. Barszcz, T., Jablonski, A.: A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mech. Syst. Signal Process 25(1), 431–451 (2011)

    Article  Google Scholar 

  64. Lei, Y., Lin, J., He, Z.: Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process 25, 1738–1749 (2011)

    Article  Google Scholar 

  65. Wang, D., Tse, P.W., Tsui, K.L.: An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process 35, 176–199 (2013)

    Article  Google Scholar 

  66. Chen, X., Feng, F., Zhang, B.: Weak fault feature extraction of rolling bearings based on an improved kurtogram. Sensors 16, 1482 (2016)

    Article  Google Scholar 

  67. Tse, P.W., Wang, D.: The design of a new sparsogram for fast bearing fault diagnosis. Mech. Syst. Signal Process 40, 499–519 (2013)

    Article  Google Scholar 

  68. Obuchowski, J., Wyłomańska, A., Zimroz, R.: Selection of informative frequency band in local damage detection in rotating machinery. Mech. Syst. Signal Process. 48(1–2), 138–152 (2014)

    Article  Google Scholar 

  69. Wang, D., Zhao, Y., Yi, C., Tsui, K.-L., Lin, J.: Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 101, 292–308 (2018)

    Article  Google Scholar 

  70. Antoni, J.: Cyclic spectral analysis in practice. Mech. Syst. Signal Process. 21(2), 597–630 (2007)

    Article  Google Scholar 

  71. Abboud, D., Baudin, S., Antoni, J., Rémond, D., Eltabach, M., Sauvage, O.: The spectral analysis of cyclo-non-stationary signals. Mech. Syst. Signal Process. 75, 280–300 (2016)

    Article  Google Scholar 

  72. Antoni, J., Xin, G., Hamzaoui, N.: Fast computation of the spectral correlation. Mech. Syst. Signal Process. 92, 248–277 (2017)

    Article  Google Scholar 

  73. Urbanek, J., Antoni, J., Barszcz, T.: Detection of signal component modulations using modulation intensity distribution. Mech. Syst. Signal Process 28, 399–413 (2012)

    Article  Google Scholar 

  74. Moshrefzadeh, A., Fasana, A., Antoni, J.: The spectral amplitude modulation: a nonlinear filtering process for diagnosis of rolling element bearings. Mech. Syst. SignalProcess. 132, 253–276 (2019)

    Article  Google Scholar 

  75. Sawalhi, N., Randall, R.B., Endo, H.: The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech. Syst. Signal Process. 21, 2616–2633 (2007)

    Article  Google Scholar 

  76. Obuchowski, J., Zimroz, R., Wylomanska, A.: Blind equalization using combined skewness-kurtosis criterion for gearbox vibration enhancement. Meas. J. Int. Meas. Conf. 88, 34–44 (2016)

    Google Scholar 

  77. Buzzoni, M., Antoni, J., D’Elia, G.: Blind deconvolution based on cyclostationarity maximization and its application to fault identification. J. Sound Vib. 432, 569–601 (2018)

    Article  Google Scholar 

  78. Peeters, C., Antoni, J., Helsen, J.: Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring. Mech. Syst. Signal Process. 138, 106556 (2020)

    Article  Google Scholar 

  79. Flandrin, P., Rilling, G., Goncalves, P.: Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11, 112–114 (2004)

    Article  Google Scholar 

  80. Antoni, J., Borghesani, P.: A statistical methodology for the design of condition indicators. Mech. Syst. Signal Process. 114, 290–327 (2019)

    Article  Google Scholar 

  81. Wang, K.: Intelligent Condition Monitoring and Diagnosis System. IOS Press, US (2003)

    MATH  Google Scholar 

  82. Liu, R., Yang, B., Zio, E., Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: A review. Mech. Syst. Signal Process. 108, 33–47 (2018)

    Article  Google Scholar 

  83. Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mech. Syst. Signal Process. 64–65, 100–131 (2015)

    Article  Google Scholar 

  84. Jia, F., Lei, Y., Lin, J., Zhou, X., Lu, N.: Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72–73, 303–315 (2016)

    Article  Google Scholar 

  85. Jinyang, J., Ming, Z., Jing, L., Kaixuan, L.: A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing 417, 36–63 (2020)

    Article  Google Scholar 

  86. Mallat S.: Understanding deep convolutional networksPhil. Trans. R. Soc. A.37420150203 (2016)

  87. Sadoughi, M., Hu, C.: Physics-based convolutional neural network for fault diagnosis of rolling element bearings. IEEE Sens. J. 19(11), 4181–4192 (2019)

    Article  Google Scholar 

  88. Zackenhouse, M., Braun, S., Feldman, M., Sidahmed, M.: Towards helicopter gearbox diagnostics from a small number of examples. Mech. Syst. Signal Process. 14(4), 523–543 (2000)

    Article  Google Scholar 

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Acknowledgements

This work was performed within the framework of the Labex CeLyA of the Université de Lyon, within the programme ‘Investissements d’Avenir’ (ANR-10-LABX-0060/ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).

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  1. University of Lyon, INSA Lyon, LVA, EA677, 69621, Villeurbanne, France

    Jérôme Antoni

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Antoni, J. A Critical Overview of the “Filterbank-Feature-Decision” Methodology in Machine Condition Monitoring. Acoust Aust 49, 177–184 (2021). https://doi.org/10.1007/s40857-021-00232-7

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