Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network
Joint Authors
Zhang, Xiaochen
Gao, Hongli
Huang, Haifeng
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-12
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
To evaluate the performance of ball screw, screw performance degradation assessment technology based on quantum genetic algorithm (QGA) and dynamic fuzzy neural network (DFNN) is studied.
The ball screw of the CINCINNATIV5-3000 machining center is treated as the study object.
Two Kistler 8704B100M1 accelerometers and a Kistler 8765A250M5 three-way accelerometer are installed to monitor the degradation trend of screw performance.
First, screw vibration signal features are extracted both in time domain and frequency domain.
Then the feature vectors can be obtained by principal component analysis (PCA).
Second, the initialization parameters of the DFNN are optimized by means of QGA.
Finally, the feature vectors are inputted to DFNN for training and then get the screw performance degradation model.
The experiment results show that the screw performance degradation model could effectively evaluate the performance of NC machine screw.
American Psychological Association (APA)
Zhang, Xiaochen& Gao, Hongli& Huang, Haifeng. 2015. Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network. Shock and Vibration،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1077971
Modern Language Association (MLA)
Zhang, Xiaochen…[et al.]. Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network. Shock and Vibration No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1077971
American Medical Association (AMA)
Zhang, Xiaochen& Gao, Hongli& Huang, Haifeng. Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network. Shock and Vibration. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1077971
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1077971