Perona-Malik Diffusion Filtering Algorithm for Mechanical Vibration Signals in Strong Noise Background
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摘要: 为了提取强噪声背景下机械振动信号的微弱故障特征,提出利用Perona-Malik非线性各向异性扩散滤波模型来实现强噪声背景信号降噪的方法。首先阐述了偏微分方程和Perona-Malik扩散滤波模型在图像降噪中的应用;其次分析了小波变换等传统信号降噪方法的不足;最后基于图像降噪和信号降噪原理的相似性,利用Perona-Malik扩散滤波模型来实现机械振动信号的降噪,将其用于轴承振动仿真信号和实测信号。实验表明,与小波阈值去噪算法等传统信号降噪方法相比,Perona-Malik扩散滤波模型更适用于强噪声背景信号降噪,同时兼顾了信号去噪和保留信号细节特征的双重要求。
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关键词:
- 小波变换 /
- 偏微分方程 /
- Perona-Malik模型 /
- 信号处理 /
- 信号降噪
Abstract: The signal denoising preprocessing is very important for extracting weak fault features from mechanical vibration signals in strong noise background. However for wavelet transform (WT) and other traditional signal processing algorithms, the processing of signal denoising is a troublesome thing. The balance between signal denoising and feature preserving is a couple of contradictions. Thus we mean to bring in the Perona-Malik nonlinear anisotropy diffusion filtering model to process signal preprocessing in strong noise. Firstly, the partial differential equation (PDE) theory is introduced, and the Perona-Malik model, as one of important nonlinear anisotropy diffusion filtering algorithms, is brought in. Secondly, from the applications in image denoising, it can be analyzed and inferred that the Perona-Malik model is a perfect solution for wavelet transform and other traditional signal denoiseing algorithms. Lastly, by comparison with the wavelet threshold denoising algorithms in the bearing vibration signals, it can be indicated that the Perona-Malik model is very appropriate for mechanical vibration signals in strong noise. Above all, the Perona-Malik filter can not only realize signal denoising but also preserve signal features with better denoising performance and without signal distortions. -
[1] Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995,41(3):613-627 [2] Kazubek M. Wavelet domain image denoising by thresholding and wiener filtering[J]. IEEE Signal Processing Letters, 2003,10(11):324-326 [3] 魏振春,王婿,徐娟.基于改进阈值自适应冗余小波的振动信号去噪[J].计算机仿真,2014,31(11):192-197 Wei Z C, Wang X, Xu J. Denoising method of vibration signal based on improved threshold and adaptive redundant second generation wavelet[J]. Computer Simulation, 2014,31(11):192-197(in Chinese) [4] 苏祖强,萧红,张毅,等.基于小波包分解与主流形识别的非线性降噪[J].仪器仪表学报,2016,37(9):1954-1961 Su Z Q, Xiao H, Zhang Y, et al. Nonlinear noise reduction method based on wavelet packet decomposition and principle manifold learning[J]. Chinese Journal of Scientific Instrument, 2016,37(9):1954-1961(in Chinese) [5] 李红延,周云龙,田峰,等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200-2206 Li H Y, Zhou Y L, Tian F, et al. Wavelet-based vibration signal de-noising algorithm with a new adaptive threshold function[J]. Chinese Journal of Scientific Instrument, 2015,36(10):2200-2206(in Chinese) [6] 周祥鑫,王小敏,杨扬,等.基于小波阈值的高速道岔振动信号降噪[J].振动与冲击,2014,33(23):200-206 Zhou X X, Wang X M, Yang Y, et al. De-noising of high-speed turnout vibration signals based on wavelet threshold[J]. Journal of Vibration and Shock, 2014,33(23):200-206(in Chinese) [7] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(7):629-639 [8] Catté F, Lions P L, Morel J M, et al. Image selective smoothing and edge detection by nonlinear diffusion[J]. SIAM Journal on Numerical Analysis, 1992,29(1):182-193 [9] Alvarez L, Lions P L, Morel J M. Image selective smoothing and edge detection by nonlinear diffusion. Ⅱ[J]. SIAM Journal on Numerical Analysis, 1992,29(3):845-866 [10] Gilboa G, Sochen N, Zeevi Y Y. Image enhancement and denoising by complex diffusion processes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26(8):1020-1036 [11] Feng L. Diffusion filtering in image processing based on wavelet transform[J]. Science in China Series F:Information Science, 2006,49(4):494-503 [12] Gilboa G, Sochen N, Zeevi Y Y. Estimation of optimal PDE-based denoising in the SNR sense[J]. IEEE Transactions on Image Processing, 2006,15(8):2269-2280 [13] Chao S M, Tsai D M. An improved anisotropic diffusion model for detail- and edge-preserving smoothing[J]. Pattern Recognition Letters, 2010,31(13):2012-2023 [14] Ren Z M, He C J, Zhang Q F. Fractional order total variation regularization for image super-resolution[J]. Signal Processing, 2013,93(9):2408-2421
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