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Shock and Vibration
Volume 2017 (2017), Article ID 9581379, 13 pages
Research Article

Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

State Key Lab of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Xiaochen Zhang

Received 24 October 2016; Revised 25 December 2016; Accepted 9 January 2017; Published 15 February 2017

Academic Editor: Lei Zuo

Copyright © 2017 Xiaochen Zhang and Dongxiang Jiang. 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.


To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA) and support vector machine (SVM) is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap), the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.