EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 26 (2025) Mechanics & Industry, 26 (2025) 22 References
Issue
Mechanics & Industry
Volume 26, 2025
Recent advances in vibrations, noise, and their use for machine monitoring
Article Number 22
Number of page(s) 19
DOI https://doi.org/10.1051/meca/2025014
Published online 01 July 2025
  1. Y. Miao, B. Zhang, J. Lin, M. Zhao, H. Liu, Z. Liu, H. Li, A review on the application of blind deconvolution in machinery fault diagnosis, Mech. Syst. Signal Process. 163, 108202 (2022) [CrossRef] [Google Scholar]
  2. R.B. Randall, J. Antoni, Rolling element bearing diagnostics - a tutorial, Mech. Syst. Signal Process. 25, 485-520 (2011) [Google Scholar]
  3. W.A. Smith, P. Borghesani, Q. Ni, K. Wang, Z. Peng, Optimal demodulation-band selection for envelope-based diagnostics: a comparative study of traditional and novel tools, Mech. Syst. Signal Process. 134, 106303 (2019) [CrossRef] [Google Scholar]
  4. S. Schmidt, K.C. Gryllias, Combining an optimisationbased frequency band identification method with historical data for novelty detection under time-varying operating conditions, Measurement 169, 108517 (2021) [CrossRef] [Google Scholar]
  5. C. Peeters, J. Antoni, J. Helsen, Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring, Mech. Syst. Signal Process. 138, 106556 (2020) [CrossRef] [Google Scholar]
  6. W. Su, F. Wang, H. Zhu, Z. Zhang, Z. Guo, Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement, Mech. Syst. Signal Process. 24, 1458-1472 (2010) [CrossRef] [Google Scholar]
  7. R. Wang, X. Ding, D. He, Q. Li, X. Li, J. Tang, W. Huang, Shift-invariant sparse filtering for bearing weak fault signal denoising, IEEE Sensors J. 23, 26096-26106 (2023) [CrossRef] [Google Scholar]
  8. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Math. Phys. Eng. Sci. 454, 903-995 (1998) [CrossRef] [MathSciNet] [Google Scholar]
  9. D. Abboud, M. Elbadaoui, W. Smith, R. Randall, Advanced bearing diagnostics: A comparative study of two powerful approaches, Mech. Syst. Signal Process. 114, 604-627 (2019) [CrossRef] [Google Scholar]
  10. D. Abboud, Y. Marnissi, M. Elbadaoui, Optimal filtering of angle-time cyclostationary signals: application to vibrations recorded under nonstationary regimes, Mech. Syst. Signal Process. 145, 106919 (2020) [CrossRef] [Google Scholar]
  11. L. Wang, W. Ye, Y. Shao, H. Xiao, A new adaptive evolutionary digital filter based on alternately evolutionary rules for fault detection of gear tooth spalling, Mech. Syst. Signal Process. 118, 645-657 (2019) [CrossRef] [Google Scholar]
  12. X. Jia, M. Zhao, Y. Di, C. Jin, J. Lee, Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement, J. Sound Vibrat. 386, 433-448 (2017) [CrossRef] [Google Scholar]
  13. P. Borghesani, N. Herwig, J. Antoni, W. Wang, A Fourierbased explanation of 1D-CNNs for machine condition monitoring applications, Mech. Syst. Signal Process. 205, 110865 (2023) [CrossRef] [Google Scholar]
  14. J. Qin, D. Yang, N. Wang, X. Ni, Convolutional sparse filter with data and mechanism fusion: a few-shot fault diagnosis method for power transformer, Eng. Appl. Artif. Intell. 124, 106606 (2023) [CrossRef] [Google Scholar]
  15. S. Schmidt, D.N. Wilke, K.C. Gryllias, Generalised envelope spectrum-based signal-to-noise objectives: formulation, optimisation and application for gear fault detection under time-varying speed conditions, Mech. Syst. Signal Process. 224, 111974 (2025) [CrossRef] [Google Scholar]
  16. M. Buzzoni, J. Antoni, G. d’Elia, Blind deconvolution based on cyclostationarity maximization and its application to fault identification, J. Sound Vibrat. 432, 569-601 (2018) [CrossRef] [Google Scholar]
  17. K. Kestel, C. Peeters, J. Antoni, J. Helsen, Fault detection via sparsity-based blind filtering on experimental vibration signals, in Annual Conference of the PHM Society, vol. 13 (2021) [CrossRef] [Google Scholar]
  18. G.L. McDonald, Q. Zhao, Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection, Mech. Syst. Signal Process. 82, 461-477 (2017) [CrossRef] [Google Scholar]
  19. Y. Cheng, B. Chen, G. Mei, Z. Wang, W. Zhang, A novel blind deconvolution method and its application to fault identification, J. Sound Vibrat. 460, 114900 (2019) [CrossRef] [Google Scholar]
  20. E. Soave, G. D’Elia, M. Cocconcelli, M. Battarra, Blind deconvolution criterion based on Fourier-Bessel series expansion for rolling element bearing diagnostics, Mech. Syst. Signal Process. 169, 108588 (2022) [CrossRef] [Google Scholar]
  21. B. Zhang, Y. Miao, J. Lin, Y. Yi, Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis, Mech. Syst. Signal Process. 158, 107736 (2021) [CrossRef] [Google Scholar]
  22. A. Saltelli, K. Aleksankina, W. Becker, P. Fennell, F. Ferretti, N. Holst, S. Li, Q. Wu, Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices, Environ. Model. Softw. 114, 29-39 (2019) [CrossRef] [Google Scholar]
  23. H. Endo, R. Randall, Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter, Mech. Syst. Signal Process. 21, 906-919 (2007) [CrossRef] [Google Scholar]
  24. C. Lopez, D. Wang, A. Naranjo, K.J. Moore, Box-cox-sparse-measures-based blind filtering: understanding the difference between the maximum kurtosis deconvolution and the minimum entropy deconvolution, Mech. Syst. Signal Process. 165, 108376 (2022) [CrossRef] [Google Scholar]
  25. Z. Liu, L. Zhang, J. Carrasco, Vibration analysis for largescale wind turbine blade bearing fault detection with an empirical wavelet thresholding method, Renewable Energy 146, 99-110 (2020) [CrossRef] [Google Scholar]
  26. L. He, D. Wang, C. Yi, Q. Zhou, J. Lin, Extracting cyclostationarity of repetitive transients from envelope spectrum based on prior-unknown blind deconvolution technique, Signal Process. 183, 107997 (2021) [CrossRef] [Google Scholar]
  27. J. Antoni, The infogram: entropic evidence of the signature of repetitive transients, Mech. Syst. Signal Process. 74, 73-94 (2016) [Google Scholar]
  28. D. Wang, W.T. Peter, K.L. Tsui, An enhanced kurtogram method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process. 35, 176-199 (2013) [CrossRef] [Google Scholar]
  29. J. Lin, L. Qu, Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis, J. Sound Vibrat. 234, 135-148 (2000) [CrossRef] [Google Scholar]
  30. K. Kestel, C. Peeters, J. Antoni, Q. Leclere, F. Girardin, J. Helsen, Informed sparsity-based blind filtering in the presence of second-order cyclostationary noise, Mech. Syst. Signal Process. 199, 110438 (2023) [CrossRef] [Google Scholar]
  31. L. He, C. Yi, D. Wang, F. Wang, J.-H. Lin, Optimized minimum generalized Lp/Lq deconvolution for recovering repetitive impacts from a vibration mixture, Measurement 168, 108329 (2021) [CrossRef] [Google Scholar]
  32. P. Borghesani, J. Antoni, CS2 analysis in presence of nongaussian background noise-effect on traditional estimators and resilience of log-envelope indicators, Mech. Syst. Signal Process. 90, 378-398 (2017) [CrossRef] [Google Scholar]
  33. P. Borghesani, P. Pennacchi, S. Chatterton, R. Ricci, The velocity synchronous discrete Fourier transform for order tracking in the field of rotating machinery, Mech. Syst. Signal Process. 44, 118-133 (2014) [CrossRef] [Google Scholar]
  34. S. Chauhan, G. Vashishtha, R. Zimroz, R. Kumar, M.K. Gupta, Optimal filter design using mountain gazelle optimizer driven by novel sparsity index and its application to fault diagnosis, Appl. Acoust. 225, 110200 (2024) [CrossRef] [Google Scholar]
  35. D. Economou, C. Mavroidis, I. Antoniadis, C. Lee, Maximally robust input preconditioning for residual vibration suppression using low-pass fir digital filters, J. Dyn. Syst. Meas. Control 124, 85-97 (2002) [CrossRef] [Google Scholar]
  36. T. Parks, J. McClellan, Chebyshev approximation for nonrecursive digital filters with linear phase, IEEE Trans. Circuit Theory 19, 189-194 (1972) [CrossRef] [Google Scholar]
  37. Z. Zhou, B. Chen, B. Huang, W. Zhang, F. Gu, A.D. Ball, X. Gong, Maximum negative entropy deconvolution and its application to bearing condition monitoring, Struct. Health Monitor. 14759217231181679 (2023) [Google Scholar]
  38. S. Schmidt, P.S. Heyns, K.C. Gryllias, An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions, Mech. Syst. Signal Process. 158, 107771 (2021) [CrossRef] [Google Scholar]
  39. W.N. Niehaus, S. Schmidt, P.S. Heyns, NIC methodology: a probabilistic methodology for improved informative frequency band identification by utilizing the available healthy historical data under time-varying operating conditions, J. Sound Vibrat. 488, 115642 (2020) [CrossRef] [Google Scholar]
  40. A. Mauricio, W.A. Smith, R.B. Randall, J. Antoni, K. Gryllias, Improved envelope spectrum via feature optimisation-gram (IESFOgram): a novel tool for rolling element bearing diagnostics under non-stationary operating conditions, Mech. Syst. Signal Process. 144, 106891 (2020) [CrossRef] [Google Scholar]
  41. J.A. Snyman, D.N. Wilke, Practical mathematical optimization: basic optimization theory and gradient-based algorithms, Springer Optimization and Its Applications (SOIA), vol. 133 (2018) [CrossRef] [Google Scholar]
  42. D.N. Wilke, S. Kok, J.A. Snyman, A.A. Groenwold, Gradient-only approaches to avoid spurious local minima in unconstrained optimization, Optim. Eng. 14, 275-304 (2013) [CrossRef] [MathSciNet] [Google Scholar]
  43. E. Borgonovo, E. Plischke, Sensitivity analysis: a review of recent advances, Eur. J. Oper. Res. 248, 869-887 (2016) [CrossRef] [Google Scholar]
  44. P. Virtanen, R. Gommers, T.E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S.J. van der Walt, M. Brett, J. Wilson, K.J. Millman, N. Mayorov, A.R.J. Nelson, E. Jones, R. Kern, E. Larson, C.J. Carey, I. Polat, Y. Feng, E.W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E.A. Quintero, C.R. Harris, A.M. Archibald, A.H. Ribeiro, F. Pedregosa, P. van Mulbregt, SciPy 1.0 Contributors, SciPy 1.0: fundamental Algorithms for Scientific Computing in Python, Nat. Methods 17, 261-272 (2020) [NASA ADS] [CrossRef] [Google Scholar]
  45. S. Schmidt, R. Zimroz, P.S. Heyns, Enhancing gearbox vibration signals under time-varying operating conditions by combining a whitening procedure and a synchronous processing method, Mech. Syst. Signal Process. 156, 107668 (2021) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.