在加性高斯白噪声的影响下,对于三阶多项式相位信号(CPS),经典的字典学习算法,如K-means Singular Value Decomposition(K-SVD),递归最小二乘字典学习算法(RLS-DLA)和K-means Singular Value Decomposition Denoising(K-SVDD)得到的学...在加性高斯白噪声的影响下,对于三阶多项式相位信号(CPS),经典的字典学习算法,如K-means Singular Value Decomposition(K-SVD),递归最小二乘字典学习算法(RLS-DLA)和K-means Singular Value Decomposition Denoising(K-SVDD)得到的学习字典,通过稀疏分解,不能有效去除信号的噪声。为此,该文提出了针对CPS去噪的字典学习算法。该算法首先利用RLS-DLA对的字典进行学习;其次采用非线性最小二乘(NLLS)法修改了该算法对字典更新的部分;最后对训练后的字典通过对信号的稀疏表示得到重构信号。对比其它的字典学习算法,该算法的信噪比(SNR)值明显高于其它算法,而均方误差(MSE)显著低于其它算法,具有明显的降噪效果。实验结果表明,采用该算法得到的字典通过稀疏分解,信号的平均信噪比比K-SVD,RLS-DLS和K-SVDD高出9.55 dB,13.94 dB和9.76 dB。展开更多
针对磁共振(magnetic resonance,MR)幅度图像中带有不易去除的与信号相关的莱斯(Rician)噪声问题,利用其复数图像中的实部与虚部所含噪声为不相关的加性高斯白噪声这一特性,代替对幅度图像直接去噪,提出将原始对偶字典学习(predual dict...针对磁共振(magnetic resonance,MR)幅度图像中带有不易去除的与信号相关的莱斯(Rician)噪声问题,利用其复数图像中的实部与虚部所含噪声为不相关的加性高斯白噪声这一特性,代替对幅度图像直接去噪,提出将原始对偶字典学习(predual dictionary learning,PDL)算法用于对MR复数图像的实部与虚部分别进行去噪,然后组合得到幅度图像的方法.经仿真实验和在HT-MRSI50-50(50 mm)1.2 T小动物核磁共振系统中的实际应用,证明所提方法较直接对幅度图像去噪取得更好的效果,在有效去除MR图像噪声的同时能较好地保持图像中的细节.与经典的字典学习算法核奇异值分解(kernel singular value decomposition,K-SVD)相比,PDL算法去噪效果优于K-SVD算法,而运算速度提高约5倍.与经典的基于非局部相似块的三维块匹配滤波(block-matching and 3D filtering,BM3D)算法相比,在噪声水平较低时PDL算法略优于BM3D算法,噪声水平较高时BM3D算法略优于PDL算法,两者总体比较接近.展开更多
The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract ...The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.展开更多
文摘针对磁共振(magnetic resonance,MR)幅度图像中带有不易去除的与信号相关的莱斯(Rician)噪声问题,利用其复数图像中的实部与虚部所含噪声为不相关的加性高斯白噪声这一特性,代替对幅度图像直接去噪,提出将原始对偶字典学习(predual dictionary learning,PDL)算法用于对MR复数图像的实部与虚部分别进行去噪,然后组合得到幅度图像的方法.经仿真实验和在HT-MRSI50-50(50 mm)1.2 T小动物核磁共振系统中的实际应用,证明所提方法较直接对幅度图像去噪取得更好的效果,在有效去除MR图像噪声的同时能较好地保持图像中的细节.与经典的字典学习算法核奇异值分解(kernel singular value decomposition,K-SVD)相比,PDL算法去噪效果优于K-SVD算法,而运算速度提高约5倍.与经典的基于非局部相似块的三维块匹配滤波(block-matching and 3D filtering,BM3D)算法相比,在噪声水平较低时PDL算法略优于BM3D算法,噪声水平较高时BM3D算法略优于PDL算法,两者总体比较接近.
基金Project(51875481) supported by the National Natural Science Foundation of ChinaProject(2682017CX011) supported by the Fundamental Research Foundations for the Central Universities,China+2 种基金Project(2017M623009) supported by the China Postdoctoral Science FoundationProject(2017YFB1201004) supported by the National Key Research and Development Plan for Advanced Rail Transit,ChinaProject(2019TPL_T08) supported by the Research Fund of the State Key Laboratory of Traction Power,China
文摘The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.
