Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network
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 characteri...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/7962828 |
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| _version_ | 1832550284886278144 |
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| author | Mingzhu Li Zhiqian Wang Jun Luo Yusheng Liu Sheng Cai |
| author_facet | Mingzhu Li Zhiqian Wang Jun Luo Yusheng Liu Sheng Cai |
| author_sort | Mingzhu Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7bf47f1dc9be41839eff909d5b44087a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-7bf47f1dc9be41839eff909d5b44087a2025-02-03T06:07:15ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/79628287962828Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural NetworkMingzhu Li0Zhiqian Wang1Jun Luo2Yusheng Liu3Sheng Cai4Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, ChinaVehicle 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.http://dx.doi.org/10.1155/2017/7962828 |
| spellingShingle | Mingzhu Li Zhiqian Wang Jun Luo Yusheng Liu Sheng Cai Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network Shock and Vibration |
| title | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_full | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_fullStr | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_full_unstemmed | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_short | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_sort | wavelet denoising of vehicle platform vibration signal based on threshold neural network |
| url | http://dx.doi.org/10.1155/2017/7962828 |
| work_keys_str_mv | AT mingzhuli waveletdenoisingofvehicleplatformvibrationsignalbasedonthresholdneuralnetwork AT zhiqianwang waveletdenoisingofvehicleplatformvibrationsignalbasedonthresholdneuralnetwork AT junluo waveletdenoisingofvehicleplatformvibrationsignalbasedonthresholdneuralnetwork AT yushengliu waveletdenoisingofvehicleplatformvibrationsignalbasedonthresholdneuralnetwork AT shengcai waveletdenoisingofvehicleplatformvibrationsignalbasedonthresholdneuralnetwork |