Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity ...Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.展开更多
Rotating machinery is widely used in the industry.They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions.Early detection of these damages is importa...Rotating machinery is widely used in the industry.They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions.Early detection of these damages is important,otherwise,they may lead to large economic loss even a catastrophe.Many signal processing methods have been developed for fault diagnosis of the rotating machinery.Local mean decomposition(LMD)is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components,namely product functions(PFs).In recent years,many researchers have adopted LMD in fault detection and diagnosis of rotating machines.We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines.First,the LMD is described.The advantages,disadvantages and some improved LMD methods are presented.Then,a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given.The review is divided into four parts:fault diagnosis of gears,fault diagnosis of rotors,fault diagnosis of bearings,and other LMD applications.In each of these four parts,a review is given to applications applying the LMD,improved LMD,and LMD-based combination methods,respectively.We give a summary of this review and some future potential topics at the end.展开更多
Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition (WPD) and local mean decomposition (LMD) permutation entropy, which ...Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition (WPD) and local mean decomposition (LMD) permutation entropy, which is based on the support vector machine (SVM) as the feature vector pattern recognition device Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions (PE) by the local mean decomposition (LMD) , and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy展开更多
The non-local means (NLM) denoising method replaces each pixel by the weighted average of pixels with the sur-rounding neighborhoods. In this paper we employ a cosine weighting function instead of the original exponen...The non-local means (NLM) denoising method replaces each pixel by the weighted average of pixels with the sur-rounding neighborhoods. In this paper we employ a cosine weighting function instead of the original exponential func-tion to improve the efficiency of the NLM denoising method. The cosine function outperforms in the high level noise more than low level noise. To increase the performance more in the low level noise we calculate the neighborhood si-milarity weights in a lower-dimensional subspace using singular value decomposition (SVD). Experimental compari-sons between the proposed modifications against the original NLM algorithm demonstrate its superior denoising per-formance in terms of peak signal to noise ratio (PSNR) and histogram, using various test images corrupted by additive white Gaussian noise (AWGN).展开更多
针对局部均值分解(Local Mean Decomposition,LMD)算法应用于电能质量扰动检测时存在“端点效应”与滑动平均收敛速度慢,严重影响测量精度的问题,提出一种改进局部均值分解方法(Modified LMD,MLMD)。通过分段三次Hermite插值取代滑动平...针对局部均值分解(Local Mean Decomposition,LMD)算法应用于电能质量扰动检测时存在“端点效应”与滑动平均收敛速度慢,严重影响测量精度的问题,提出一种改进局部均值分解方法(Modified LMD,MLMD)。通过分段三次Hermite插值取代滑动平均法,有效改善LMD收敛慢、受平滑长度影响的弊端。为避免延拓长度不够而导致的“延拓失败”情形,在镜像延拓法的基础上结合“奇延拓”方法提出改进镜像延拓法。针对“直接法”求频率存在“毛刺现象”的弊端,文中改用希尔伯特变换(Hilbert Transform,HT)求取瞬时频率。最后,将MLMD分别应用于单一扰动信号与复合谐波信号的检测,相较传统的经验模态分解方法(Empirical Mode Decomposition,EMD),MLMD方法可有效抑制“端点效应”,同时能更准确的定位扰动信号的起止时刻,并且对高次谐波信号有更好的提取能力。展开更多
为解决包裹相位图中存留的散斑噪声问题,文中提出了一种基于正余弦分解的两分段自适应非局部均值滤波方法。该方法通过两次改进衰减参数的大小和相似性度量的方式实现了算法的自适应化。利用该方法对包裹相位图的正余弦分量去噪,去噪后...为解决包裹相位图中存留的散斑噪声问题,文中提出了一种基于正余弦分解的两分段自适应非局部均值滤波方法。该方法通过两次改进衰减参数的大小和相似性度量的方式实现了算法的自适应化。利用该方法对包裹相位图的正余弦分量去噪,去噪后利用反正切运算获取干净的包裹相位,对该相位进行解包裹运算。实验和仿真结果表明,所提方法既有效去除了包裹相位图中的噪声,也保留了相位图中的边缘信息。相比于分别使用SCA(Sine Cosine Algorithm)方法和BM3D(Block-Matching and 3D filtering)方法,通过所提方法去噪后的图像等效视数(Equivalent Number of Looks,ENL)最大,散斑抑制指数(Speckle Suppression Index,SSI)最小,且均方误差提升了约两倍,说明所提方法有效去除了包裹相位中的噪声,提高了相位解包裹的精度。展开更多
振动信号分析是轴承故障诊断中的重要技术手段之一。变转速工况下的滚动轴承振动信号是典型的非平稳信号,并且在转频变化较小的工况中还存在噪声干扰的问题,使传统的时频分析技术难以应用。为解决该问题,提出了一种基于经验最优包络(emp...振动信号分析是轴承故障诊断中的重要技术手段之一。变转速工况下的滚动轴承振动信号是典型的非平稳信号,并且在转频变化较小的工况中还存在噪声干扰的问题,使传统的时频分析技术难以应用。为解决该问题,提出了一种基于经验最优包络(empirical optimal envelope,EOE)的局部均值分解(local mean decomposition,LMD)和采用分段线性插值的计算阶次跟踪(computing order tracking,COT)算法相结合的故障诊断方法。首先,确定低通滤波器的截止频率和滤波阶数,对滚动轴承振动信号进行滤波,并对滤波后的包络信号进行COT,以获得角域平稳信号。然后,利用EOE_LMD对重采样后的平稳信号进行处理,得到若干乘积函数(product function,PF)分量。最后,通过计算各分量的信息熵和相关系数,选取合适的分量进行阶次分析,以判断变转速滚动轴承的故障类型。结果表明,该方法可以消除转速波动对故障特征提取的影响,在不同转速变化条件下对滚动轴承具有良好的故障诊断能力。展开更多
为解决混合储能系统的光伏输出功率波动性较大的问题,提出一种改进的局部均值分解(improved local mean decomposition, ILMD)的功率分配方案。对光伏发电出力进行平滑处理,可得到满足国家要求的光伏并网功率,利用ILMD对混合储能功率进...为解决混合储能系统的光伏输出功率波动性较大的问题,提出一种改进的局部均值分解(improved local mean decomposition, ILMD)的功率分配方案。对光伏发电出力进行平滑处理,可得到满足国家要求的光伏并网功率,利用ILMD对混合储能功率进行分解,确定其高频功率和低频功率并分别分配给超级电容和蓄电池,建立具有目标函数的功率优化模型,最大限度地降低整个系统全生命周期的投资成本,使用改进鲸鱼优化算法求解获得符合优化模型要求的容量配置。通过算例分析,对比不同的储能容量配置策略,验证所提策略的可行性。展开更多
基金The authors gratefully acknowledge the support of the National Natural Science Foundation of China(No.11574250).
文摘Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.
基金supported by the National Natural Science Foundation of China(5180543471771186+4 种基金71631001)the Postdoctoral Innovative Talent Plan of China(BX20180257)the Postdoctoral Science Funds of China(2018M641021)the Key Research Program of Shaanxi Province(2019KW-017)the Natural Science and Engineering Research Council of Canada(RGPIN-2019-05361)
文摘Rotating machinery is widely used in the industry.They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions.Early detection of these damages is important,otherwise,they may lead to large economic loss even a catastrophe.Many signal processing methods have been developed for fault diagnosis of the rotating machinery.Local mean decomposition(LMD)is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components,namely product functions(PFs).In recent years,many researchers have adopted LMD in fault detection and diagnosis of rotating machines.We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines.First,the LMD is described.The advantages,disadvantages and some improved LMD methods are presented.Then,a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given.The review is divided into four parts:fault diagnosis of gears,fault diagnosis of rotors,fault diagnosis of bearings,and other LMD applications.In each of these four parts,a review is given to applications applying the LMD,improved LMD,and LMD-based combination methods,respectively.We give a summary of this review and some future potential topics at the end.
基金supported by the National Natural Science Foundation of China(51375405)Independent Project of the State Key Laboratory of Traction Power(2016TP-10)
文摘Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition (WPD) and local mean decomposition (LMD) permutation entropy, which is based on the support vector machine (SVM) as the feature vector pattern recognition device Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions (PE) by the local mean decomposition (LMD) , and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy
文摘The non-local means (NLM) denoising method replaces each pixel by the weighted average of pixels with the sur-rounding neighborhoods. In this paper we employ a cosine weighting function instead of the original exponential func-tion to improve the efficiency of the NLM denoising method. The cosine function outperforms in the high level noise more than low level noise. To increase the performance more in the low level noise we calculate the neighborhood si-milarity weights in a lower-dimensional subspace using singular value decomposition (SVD). Experimental compari-sons between the proposed modifications against the original NLM algorithm demonstrate its superior denoising per-formance in terms of peak signal to noise ratio (PSNR) and histogram, using various test images corrupted by additive white Gaussian noise (AWGN).
文摘为解决包裹相位图中存留的散斑噪声问题,文中提出了一种基于正余弦分解的两分段自适应非局部均值滤波方法。该方法通过两次改进衰减参数的大小和相似性度量的方式实现了算法的自适应化。利用该方法对包裹相位图的正余弦分量去噪,去噪后利用反正切运算获取干净的包裹相位,对该相位进行解包裹运算。实验和仿真结果表明,所提方法既有效去除了包裹相位图中的噪声,也保留了相位图中的边缘信息。相比于分别使用SCA(Sine Cosine Algorithm)方法和BM3D(Block-Matching and 3D filtering)方法,通过所提方法去噪后的图像等效视数(Equivalent Number of Looks,ENL)最大,散斑抑制指数(Speckle Suppression Index,SSI)最小,且均方误差提升了约两倍,说明所提方法有效去除了包裹相位中的噪声,提高了相位解包裹的精度。
文摘振动信号分析是轴承故障诊断中的重要技术手段之一。变转速工况下的滚动轴承振动信号是典型的非平稳信号,并且在转频变化较小的工况中还存在噪声干扰的问题,使传统的时频分析技术难以应用。为解决该问题,提出了一种基于经验最优包络(empirical optimal envelope,EOE)的局部均值分解(local mean decomposition,LMD)和采用分段线性插值的计算阶次跟踪(computing order tracking,COT)算法相结合的故障诊断方法。首先,确定低通滤波器的截止频率和滤波阶数,对滚动轴承振动信号进行滤波,并对滤波后的包络信号进行COT,以获得角域平稳信号。然后,利用EOE_LMD对重采样后的平稳信号进行处理,得到若干乘积函数(product function,PF)分量。最后,通过计算各分量的信息熵和相关系数,选取合适的分量进行阶次分析,以判断变转速滚动轴承的故障类型。结果表明,该方法可以消除转速波动对故障特征提取的影响,在不同转速变化条件下对滚动轴承具有良好的故障诊断能力。
文摘为解决混合储能系统的光伏输出功率波动性较大的问题,提出一种改进的局部均值分解(improved local mean decomposition, ILMD)的功率分配方案。对光伏发电出力进行平滑处理,可得到满足国家要求的光伏并网功率,利用ILMD对混合储能功率进行分解,确定其高频功率和低频功率并分别分配给超级电容和蓄电池,建立具有目标函数的功率优化模型,最大限度地降低整个系统全生命周期的投资成本,使用改进鲸鱼优化算法求解获得符合优化模型要求的容量配置。通过算例分析,对比不同的储能容量配置策略,验证所提策略的可行性。