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Shock and Vibration
Volume 2015, Article ID 167902, 9 pages
http://dx.doi.org/10.1155/2015/167902
Research Article

Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

1School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
2Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA

Received 30 March 2015; Accepted 16 July 2015

Academic Editor: Chuan Li

Copyright © 2015 Weigang Wen 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 [6 citations]

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

  • Aditya Sharma, M. Amarnath, and Pavan Kumar Kankar, “Novel ensemble techniques for classification of rolling element bearing faults,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2016. View at Publisher · View at Google Scholar
  • Lei Zhang, Long Zhang, Junfeng Hu, and Guoliang Xiong, “Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy,” Shock and Vibration, vol. 2016, pp. 1–13, 2016. View at Publisher · View at Google Scholar
  • Weidong Cheng, Weigang Wen, Dezun Zhao, and Jianyong Li, “An improved resampling algorithm for rolling element bearing fault diagnosis under variable rotational speeds,” Journal of Southeast University (English Edition), vol. 33, no. 2, pp. 150–158, 2017. View at Publisher · View at Google Scholar
  • Laha, “Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising,” Measurement: Journal of the International Measurement Confederation, vol. 100, pp. 157–163, 2017. View at Publisher · View at Google Scholar
  • Shengli Zhang, and J. Tang, “Integrating angle-frequency domain synchronous averaging technique with feature extraction for gear fault diagnosis,” Mechanical Systems and Signal Processing, vol. 99, pp. 711–729, 2018. View at Publisher · View at Google Scholar
  • Pei Cao, Shengli Zhang, and J. Tang, “Preprocessing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning,” IEEE Access, pp. 1–1, 2018. View at Publisher · View at Google Scholar