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Shock and Vibration
Volume 2015, Article ID 150797, 11 pages
Research Article

Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received 16 April 2015; Revised 23 June 2015; Accepted 1 July 2015

Academic Editor: Wahyu Caesarendra

Copyright © 2015 Xiaochen Zhang 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 [4 citations]

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

  • Xiaochen Zhang, Dongxiang Jiang, Te Han, and Nanfei Wang, “Feature dimension reduction method of rolling bearing based on quantum genetic algorithm,” 2016 Prognostics and System Health Management Conference (PHM-Chengdu), pp. 1–5, . View at Publisher · View at Google Scholar
  • David Alejandro Elvira-Ortiz, Rene de Jesus Romero-Troncoso, Arturo Yosimar Jaen-Cuellar, Luis Morales-Velazquez, and Roque Alfredo Osornio-Rios, “Vibration Suppression for Improving the Estimation of Kinematic Parameters on Industrial Robots,” Shock and Vibration, vol. 2016, pp. 1–15, 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
  • Li Zhang, Hongli Gao, Dawei Dong, Guoqiang Fu, and Qi Liu, “Wear Calculation-Based Degradation Analysis and Modeling for Remaining Useful Life Prediction of Ball Screw,” Mathematical Problems in Engineering, vol. 2018, pp. 1–18, 2018. View at Publisher · View at Google Scholar