| 基于小波包惩罚函数的烟机振动信号软阈值降噪 |
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| 引用本文： | 陈涛,徐小力,王少红.基于小波包惩罚函数的烟机振动信号软阈值降噪[J].北京理工大学学报,2010,30(8):906-910. |
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| 作者姓名： | 陈涛 徐小力 王少红 |
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| 作者单位： | 北京理工大学,机械与车辆学院,北京,100081;北京理工大学,机械与车辆学院,北京,100081;北京信息科技大学现代测控教育部重点实验室,北京,100192 |
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| 基金项目： | 国家自然科学基金资助项目，北京市人才强教深化计划项目 |
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| 摘 要： | 为解决烟机振动信号受到噪声干扰这一问题,研究基于小波包阈值降噪的原理和方法,给出了小波包阈值降噪的步骤,阐述了Birgé-Massart惩罚函数确定阈值的原则和软阈值的量化处理,分析了阈值、信噪比和均方误差随惩罚因子的变化规律.并将基于小波包惩罚函数的软阈值降噪与Rigrsure、Heursure、Sqtwolog、Minimax4种阈值降噪方法进行了比较.结果表明基于惩罚函数的小波包软阈值方法能有效降低噪声.基于该方法的烟机振动信号降噪在保留信号突变部分的同时,具有良好的光滑性.
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| 关 键 词： | 降噪 振动信号 小波包 软阈值 惩罚函数 |
| 收稿时间： | 2009/10/11 0:00:00 |
Method of Wavelet Packet-Based Penalty Function Soft-Threshold to De-Noise Vibration Signals for Flue Gas Turbine |
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| CHEN Tao,XU Xiao-li and WANG Shao-hong.Method of Wavelet Packet-Based Penalty Function Soft-Threshold to De-Noise Vibration Signals for Flue Gas Turbine[J].Journal of Beijing Institute of Technology(Natural Science Edition),2010,30(8):906-910. |
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| Authors: | CHEN Tao XU Xiao-li and WANG Shao-hong |
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| Institution: | School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, China;School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, China |
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| Abstract: | To solve the problem of vibration signals acquired from flue gas turbine interfered by noise, the principle and method of wavelet packet-based penalty function soft-threshold de-noising are analyzed and its procedure of de-noising is provided. The rule of Birgé-Massart penalty function to determine threshold is clarified and soft-threshold quantization processing is made. Then, the value changes of threshold, SNR and mean square error with the penalty factor are observed and compared with Rigrsure, Heursure, Sqtwolog, Minimax soft-threshold de-noising methods. The results show that the penalty function soft-threshold method can effectively reduce the noise. The de-noised vibration signals of flue gas turbine based on this method can retain mutant part of the signal, while it still has good smoothness. |
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| Keywords: | de-noising vibration signals wavelet packet soft threshold penalty function |
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