Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network
Joint Authors
Li, Mingzhu
Wang, Zhiqian
Luo, Jun
Liu, Yusheng
Cai, Sheng
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-26
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Vehicle Platform Vibration Signal (VPVS) denoising is essential to achieve high measurement accuracy of precise optical measuring instrument (POMI).
A method to denoise the VPVS is proposed based on the wavelet coefficients thresholding and threshold neural network (TNN).
According to the characteristics of VPVS, a novel thresholding function is constructed, and then its optimized threshold is selected through unsupervised learning of TNN.
The original VPVS mixed in trend and random noise is constructed as VPVS model.
A VPVS denoising flow is proposed based on the power spectral and energy distribution of the VPVS model.
The simulation shows that the proposed denoising method achieves better results, compared to the previous denoising methods using the indexes of SNR and RMSE.
The experiment demonstrates that it is efficient for denoising VPVS polluted by the trend and random noise.
American Psychological Association (APA)
Li, Mingzhu& Wang, Zhiqian& Luo, Jun& Liu, Yusheng& Cai, Sheng. 2017. Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network. Shock and Vibration،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1205042
Modern Language Association (MLA)
Li, Mingzhu…[et al.]. Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network. Shock and Vibration No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1205042
American Medical Association (AMA)
Li, Mingzhu& Wang, Zhiqian& Luo, Jun& Liu, Yusheng& Cai, Sheng. Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network. Shock and Vibration. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1205042
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205042