Open Access
Issue
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
Article Number 02017
Number of page(s) 7
Section Smart Manufacturing and Industrial 4.0
DOI https://doi.org/10.1051/matecconf/201925502017
Published online 16 January 2019
  1. Z. Li, Y. Jiang, C. Hu, Z. Peng, Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review, Meas. J. Int. Meas. Confed. 90 (2016) 4–19. doi:10.1016/j.measurement.2016.04.036. [CrossRef] [Google Scholar]
  2. H. Zhao, M. Sun, W. Deng, X. Yang, A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing, Entropy. 19 (2017). doi:10.3390/e19010014. [Google Scholar]
  3. X. Xue, J. Zhou, A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery, ISA Trans. (2016). doi:10.1016/j.isatra.2016.10.014. [Google Scholar]
  4. J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, D. Siegel, Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications, Mech. Syst. Signal Process. 42 (2014) 314–334. doi:10.1016/j.ymssp.2013.06.004. [CrossRef] [Google Scholar]
  5. Y. Yu, Yu Dejie, C. Junsheng, A roller bearing fault diagnosis method based on EMD energy entropy and ANN, J. Sound Vib. 294 (2006) 269–277. doi: https://doi.org/10.1016/j.jsv.2005.11.002. [CrossRef] [Google Scholar]
  6. J. Cheng, D. Yu, J. Tang, Y. Yang, Local rub-impact fault diagnosis of the rotor systems based on EMD, Mech. Mach. Theory. 44 (2009) 784–791. doi:10.1016/j.mechmachtheory.2008.04.006. [CrossRef] [Google Scholar]
  7. C. Junsheng, Y. Dejie, Y. Yu, A fault diagnosis approach for roller bearings based on EMD method and AR model, Mech. Syst. Signal Process. 20 (2006) 350–362. doi:10.1016/j.ymssp.2004.11.002. [CrossRef] [Google Scholar]
  8. N.E. Huang, Z. Wu, Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method, Adv. Adapt. Data Anal. 01 (2009) 1. doi:10.1142/S1793536909000047. [CrossRef] [Google Scholar]
  9. J.-R. Yeh, J.-S. Shieh, Complementary Ensemble Empirical Mode Decomposition: A Novel Enhanced Data Analysis Method, Adv. Adapt. Data Anal. 2 (2010) 135–156. doi:10.1142/S1793536910000422. [CrossRef] [MathSciNet] [Google Scholar]
  10. J. Zheng, J. Cheng, Y. Yang, Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing, Signal Processing. 96 (2014) 362–374. doi:10.1016/j.sigpro.2013.09.013. [CrossRef] [Google Scholar]
  11. H. Li, Y. Hu, F. Li, G. Meng, Succinct and fast empirical mode decomposition, Mech. Syst. Signal Process. 85 (2017) 879–895. doi:10.1016/j.ymssp.2016.09.031. [CrossRef] [Google Scholar]
  12. J.S. Smith, The local mean decomposition and its application to EEG perception data, J. R. Soc. Interface. 2 (2005) 443–454. doi:10.1098/rsif.2005.0058. [CrossRef] [PubMed] [Google Scholar]
  13. M.G. Frei, I. Osorio, Intrinsic time-scale decomposition?: time - frequency - energy analysis and real-time filtering of non-stationary signals, Proc. R. Soc. A. (2007) 321–342. doi:10.1098/rspa.2006.1761. [CrossRef] [Google Scholar]
  14. J. Cheng, Y. Yang, Y. Yang, Local characteristic-scale decomposition method and its application to gear fault diagnosis, Jixie Gongcheng Xuebao/Journal Mech. Eng. 48 (2012) 64–71. doi:10.3901/JME.2012.09.064. [CrossRef] [Google Scholar]
  15. Y. Li, M. Xu, Y. Wei, W. Huang, Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition, Mamt. 94 (2015) 9–27. doi:10.1016/j.mechmachtheory.2015.08.001. [Google Scholar]
  16. A. Hu, X. Yan, L. Xiang, A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT- fractal dimension, Renew. Energy. 83 (2015) 767–778. doi:10.1016/j.renene.2015.04.063. [CrossRef] [Google Scholar]
  17. K. Dragomiretskiy, D. Zosso, Variational Mode Decomposition, IEEE Trans. Signal Process. 62 (2014) 531–544. doi:10.1109/tsp.2013.2288675. [NASA ADS] [CrossRef] [Google Scholar]
  18. M.F. Isham, M.S. Leong, M.H. Lim, Z.A. Ahmad, Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals, Trans. Nanjing Univ. Aero. Astro. 35 (2018) 38–50. doi:10.16356/j.1005-1120.2018.01.038. [Google Scholar]
  19. S. Lahmiri, A. Shmuel, Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages, Opt. Laser Technol. 96 (2017) 243–248. doi: http://dx.doi.org/10.1016/j.optlastec.2017.05.012. [CrossRef] [Google Scholar]
  20. S. Lahmiri, Intraday stock price forecasting based on variational mode decomposition, J. Comput. Sci. 12 (2016) 23–27. doi: https://doi.org/10.1016/j.jocs.2015.11.011. [CrossRef] [Google Scholar]
  21. A. Upadhyay, R.B. Pachori, Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition, J. Franklin Inst. 352 (2015) 2679–2707. doi: https://doi.org/10.1016/j.jfranklin.2015.04.001. [CrossRef] [Google Scholar]
  22. M. Zhang, Z. Jiang, K. Feng, Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump, Mech. Syst. Signal Process. 93 (2017) 460–493. doi: https://doi.org/10.1016/j.ymssp.2017.02.013. [CrossRef] [Google Scholar]
  23. Mohanty, K.K. Gupta, K.S. Raju, Bearing fault analysis using variational mode decomposition, in: 2014 9th Int. Conf. Ind. Inf. Syst., 2014: pp. 1–6. doi:10.1109/ICIINFS.2014.7036617. [Google Scholar]
  24. S. Mohanty, K.K. Gupta, K.S. Raju, Comparative study between VMD and EMD in bearing fault diagnosis, in: 2014 9th Int. Conf. Ind. Inf. Syst., 2014: pp. 1–6. doi:10.1109/ICIINFS.2014.7036515. [Google Scholar]
  25. Z. Jinde, J. Zhanwei, P. Ziwei, Z. Kang, VMD based adaptive multiscale fuzzy entropy and its application to rolling bearing fault diagnosis, Proc. Int. Conf. Sens. Technol. ICST. (2016) 0–3. doi:10.1109/ICSensT.2016.7796267. [Google Scholar]
  26. H. Zhao, L. Li, Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator, IET Renew. Power Gener. 11 (2016) 453–460. doi:10.1049/iet-rpg.2016.0070. [CrossRef] [Google Scholar]
  27. Z. Li, J. Chen, Y. Zi, J. Pan, Independence- oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive, Mech. Syst. Signal Process. 85 (2017) 512–529. doi:10.1016/j.ymssp.2016.08.042. [CrossRef] [Google Scholar]
  28. Z. Li, Y. Jiang, Q. Guo, C. Hu, Z. Peng, Multi- dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations, Renew. Energy. (n.d.). doi: https://doi.org/10.1016/j.renene.2016.12.013. [Google Scholar]
  29. H. Zhao, L. Li, Fault diagnosis of wind turbine bearing based on variational mode decomposition and Spectrum Kurtosis, IET Renew. Power Gener. (2016) 851–854. doi:10.1049/iet-rpg.2016.0070. [Google Scholar]
  30. G. Tang, G. Luo, W. Zhang, C. Yang, H. Wang, Underdetermined blind source separation with variational mode decomposition for compound roller bearing fault signals, Sensors (Switzerland). 16 (2016). doi:10.3390/s16060897. [Google Scholar]
  31. X. An, H. Zeng, Fault diagnosis method for spherical roller bearing of wind turbine based on variational mode decomposition and singular value decomposition., J. Vibroengineering. 18 (2016) 3548–3556. http://10.0.84.91/jve.2016.16553. [CrossRef] [Google Scholar]
  32. X. An, L. Pan, Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy, Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 231 (2017) 200–206. doi:10.1177/1748006X17693492. [Google Scholar]
  33. C. Yi, Y. Lv, Z. Dang, A fault diagnosis scheme for rolling bearing based on particle swarm optimization in variational mode decomposition, Shock Vib. 2016 (2016). doi:10.1155/2016/9372691. [Google Scholar]
  34. J. Zhu, C. Wang, Z. Hu, F. Kong, X. Liu, Adaptive variational mode decomposition based on artificial fish swarm algorithm for fault diagnosis of rolling bearings, Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 231 (2017) 635–654. doi:10.1177/0954406215623311. [CrossRef] [Google Scholar]
  35. A. Muralidharan, V. Sugumaran, K.P. Soman, M. Amarnath, Fault diagnosis of helical gear box using variational mode decomposition and random forest algorithm, SDHM Struct. Durab. Heal. Monit. 10 (2015) 55–80. http://www.scopus.com/inward/record.url?eid=2-s2.0-84926337674&partnerID=tZOtx3y1. [Google Scholar]
  36. X. An, H. Zeng, C. Li, Envelope demodulation based on variational mode decomposition for gear fault diagnosis, Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. (2016) 0954408916644271. doi:10.1177/0954408916644271. [Google Scholar]
  37. H. Mahgoun, F. Chaari, A. Felkaoui, Detection of gear faults in variable rotating speed using variational mode decomposition (VMD), Mech. Ind. 17 (2016). https://doi.org/10.1051/meca/2015058. [Google Scholar]
  38. X. Yan, M. Jia, L. Xiang, Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum, Meas. Sci. Technol. 27 (2016) 75002. http://stacks.iop.org/0957-0233/27/i=7/a=075002. [CrossRef] [Google Scholar]
  39. D. Zhang, Z. Feng, Application of variational mode decomposition based demodulation Analysis in gearbox fault diagnosis, Conf. Rec. - IEEE Instrum. Meas. Technol. Conf. 2016-July (2016). doi:10.1109/I2MTC.2016.7520586. [Google Scholar]
  40. Z. Feng, D. Zhang, M. Zuo, Planetary Gearbox Fault diagnosis via Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition, Appl. Sci. 7 (2017) 775. doi:10.3390/app7080775. [CrossRef] [Google Scholar]
  41. Y. Li, G. Cheng, C. Liu, X. Chen, Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks, Meas. J. Int. Meas. Confed. 130 (2018) 94–104. doi:10.1016/j.measurement.2018.08.002. [CrossRef] [Google Scholar]
  42. F. Jiang, Z. Zhu, W. Li, S. Xia, G. Zhou, Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition, J. Vibroengineering. 19 (2017) 6021–6035. [CrossRef] [Google Scholar]
  43. H. Yang, S. Liu, H. Zhang, Adaptive estimation of VMD modes number based on cross correlation coefficient, J. Vibroengineering. 19 (2017) 1185–1196. [CrossRef] [Google Scholar]
  44. G. Ren, J. Jia, J. Mei, X. Jia, J. Han, Y. Wang, An improved variational mode decomposition method and its application in diesel engine fault diagnosis, J. Vibroengineering. 20 (2018) 2363–2378. [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.