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Mathematical Problems in Engineering
Volume 2018, Article ID 4598706, 10 pages
Research Article

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

1State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, Shanxi 710048, China
2Gansu Province Electric Power Research Institute, Lanzhou, Gansu 730050, China

Correspondence should be addressed to Fuqi Ma; moc.361@67174629381

Received 14 September 2017; Revised 21 January 2018; Accepted 4 February 2018; Published 7 March 2018

Academic Editor: Frederic Kratz

Copyright © 2018 Rong Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • He Yu, Hongru Li, Yaolong Li, and Yifan Li, “A Novel Improved Full Vector Spectrum Algorithm and its Application in Multi-sensor Data Fusion for Hydraulic Pumps,” Measurement, 2018. View at Publisher · View at Google Scholar
  • Riyadh Nazar Ali Algburi, and Hongli Gao, “Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal,” Energies, vol. 12, no. 14, pp. 2816, 2019. View at Publisher · View at Google Scholar
  • Shengnan Tang, Wanlu Jiang, Yong Zhu, Lingxiao Quan, and Ling Zhou, “Extraction method for signal effective component based on extreme-point symmetric mode decomposition and Kullback–Leibler divergence,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 41, no. 2, 2019. View at Publisher · View at Google Scholar