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Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine
Joint Authors
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-15
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Xiaochen& Jiang, D. X.. 2017. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine. Shock and Vibration،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1205189
Modern Language Association (MLA)
Zhang, Xiaochen& Jiang, D. X.. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine. Shock and Vibration No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1205189
American Medical Association (AMA)
Zhang, Xiaochen& Jiang, D. X.. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine. Shock and Vibration. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1205189
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205189