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Shock and Vibration
Volume 20 (2013), Issue 2, Pages 263-272
http://dx.doi.org/10.3233/SAV-2012-00742

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

A. Moosavian, H. Ahmadi, A. Tabatabaeefar, and M. Khazaee

Department of Mechanical Engineering of Agricultural Machinery, University of Tehran, Karaj, Iran

Received 18 March 2012; Revised 1 August 2012

Copyright © 2013 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 [13 citations]

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

  • Ashkan Moosavian, Hojat Ahmadi, Babak Sakhaei, and Reza Labbafi, “Support vector machine and K-nearest neighbour for unbalanced fault detection,” Journal of Quality in Maintenance Engineering, vol. 20, no. 1, pp. 65–75, 2014. View at Publisher · View at Google Scholar
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  • 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
  • Lilian Shi, “Correlation Coefficient of Simplified Neutrosophic Sets for Bearing Fault Diagnosis,” Shock and Vibration, vol. 2016, pp. 1–11, 2016. View at Publisher · View at Google Scholar
  • Yilun Liu, Dalian Yang, and Jie Tao, “Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion,” Shock and Vibration, vol. 2016, 2016. View at Publisher · View at Google Scholar
  • Li Zhang, Hongli Gao, Juan Wen, Shichao Li, and Qi Liu, “A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion,” Microelectronics Reliability, 2017. View at Publisher · View at Google Scholar
  • Jun-hong Zhang, and Yu Liu, “Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines,” Frontiers of Information Technology & Electronic Engineering, vol. 18, no. 2, pp. 272–286, 2017. View at Publisher · View at Google Scholar