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Shock and Vibration
Volume 19 (2012), Issue 6, Pages 1373-1383
http://dx.doi.org/10.3233/SAV-2012-0679

Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers

Pei-Lin Zhang,1 Bing Li,1,2 Shuang-Shan Mi,2 Ying-Tang Zhang,1 and Dong-Sheng Liu2

1First Department, Mechanical Engineering College, Shijiazhuang, Hebei, China
2Forth Department, Mechanical Engineering College, Shijiazhuang, Hebei, China

Received 4 November 2010; Revised 29 October 2011

Copyright © 2012 Hindawi Publishing Corporation. 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 [8 citations]

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

  • Shaojiang Dong, Shirong Yin, Baoping Tang, Lili Chen, and Tianhong Luo, “Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model,” Shock and Vibration, vol. 2014, pp. 1–15, 2014. View at Publisher · View at Google Scholar
  • A. Soleimani, and S.E. Khadem, “Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets,” Chaos, Solitons & Fractals, vol. 78, pp. 61–75, 2015. View at Publisher · View at Google Scholar
  • Weigang Wen, Zhaoyan Fan, Donald Karg, and Weidong Cheng, “Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features,” Shock and Vibration, vol. 2015, pp. 1–9, 2015. View at Publisher · View at Google Scholar
  • Aleksandra Ziaja, Ifigeneia Antoniadou, Tomasz Barszcz, Wieslaw J. Staszewski, and Keith Worden, “Fault detection in rolling element bearings using wavelet-based variance analysis and novelty detection,” Journal Of Vibration And Control, vol. 22, no. 2, pp. 396–411, 2016. View at Publisher · View at Google Scholar
  • Shazali Osman, and Wilson Wang, “A Morphological Hilbert-Huang Transform Technique for Bearing Fault Detection,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 11, pp. 2646–2656, 2016. View at Publisher · View at Google Scholar
  • Guiji Tang, Xiaolong Wang, and Yuling He, “Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution,” Journal of Mechanical Science and Technology, vol. 30, no. 1, pp. 43–54, 2016. View at Publisher · View at Google Scholar
  • Liang Guo, Hongli Gao, Haifeng Huang, Xiang He, and ShiChao Li, “Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring,” Shock And Vibration, 2016. View at Publisher · View at Google Scholar
  • Juan Jose Saucedo-Dorantes, Miguel Delgado-Prieto, Juan Antonio Ortega-Redondo, Roque Alfredo Osornio-Rios, and Rene de Jesus Romero-Troncoso, “Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain,” Shock and Vibration, vol. 2016, pp. 1–13, 2016. View at Publisher · View at Google Scholar