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
This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing...This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.展开更多
在爆破振动监测过程中,为解决低频趋势成分干扰所引起的基线漂移问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition, RLMD)和均值判比(mean ratio, MR)方法的爆破振动信号基线校正方法。首先,利用RLMD对包含趋势项...在爆破振动监测过程中,为解决低频趋势成分干扰所引起的基线漂移问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition, RLMD)和均值判比(mean ratio, MR)方法的爆破振动信号基线校正方法。首先,利用RLMD对包含趋势项的振动信号进行自适应分解,生成一系列乘积函数(product functions, PF);随后,通过MR方法筛选出低频趋势项分量,去除这些成分并重构剩余信号,以校正基线漂移。仿真信号分析结果表明,与传统的最小二乘拟合法(ordinary least squares, OLS)和局部均值分解(local mean decomposition, LMD)相比,RLMD方法在提取趋势项方面具有更高的准确性和稳定性,有效避免了模态混叠现象。现场爆破振动监测试验结果显示,与远区振动信号相比,近区实测爆破振动信号受到低频趋势项的干扰更为严重。通过RLMD-MR方法进行基线校正后,信号波形能够有效恢复至基线中心附近,解决了基线漂移问题。展开更多
针对传统相位差分法在振动定位中因信噪比不足导致的定位精度低和信号恢复困难问题,提出一种基于相位敏感光时域反射仪(Φ-OTDR)的滑动方差变分模态分解(SV-VMD)振动定位及信号恢复方法。SV算法通过滑动窗口计算相位差分方差,选取振动点...针对传统相位差分法在振动定位中因信噪比不足导致的定位精度低和信号恢复困难问题,提出一种基于相位敏感光时域反射仪(Φ-OTDR)的滑动方差变分模态分解(SV-VMD)振动定位及信号恢复方法。SV算法通过滑动窗口计算相位差分方差,选取振动点前10 m的相位作为修正参考相位,有效抑制累积噪声;VMD通过多尺度分解分离有效信号分量与趋势噪声,消除正交解调引入的直流偏移。实验结果表明:与传统相位差分法相比,SV算法使系统信噪比提升12 d B(从14 d B至26 d B),并在多振动源场景下清晰分辨了400 m和650 m处的干扰事件;经参数优化(惩罚因子α=100,模式数K=3)的VMD算法成功恢复了三角波信号,显著抑制了非振动相关噪声。展开更多
针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分...针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分解,获取模态分量。接着,将原始信号与这些模态分量分割为多个时间局部片段,并计算它们对应时间局部片段的相关熵值。然后,通过LWT算法处理弱相关分量,最后重构剩余分量以完成去噪过程。实验结果表明:在5 km的传感距离和10 m的空间分辨率的条件下,系统的信噪比达到了54.36 d B,同时均方根误差降低至0.091。展开更多
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(6137116961601167)+2 种基金the Jiangsu Natural Science Foundation(BK20161489)the open research fund of State Key Laboratory of Millimeter Waves,Southeast University(K201826)the Fundamental Research Funds for the Central Universities(NE2017103)
文摘This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.
文摘在爆破振动监测过程中,为解决低频趋势成分干扰所引起的基线漂移问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition, RLMD)和均值判比(mean ratio, MR)方法的爆破振动信号基线校正方法。首先,利用RLMD对包含趋势项的振动信号进行自适应分解,生成一系列乘积函数(product functions, PF);随后,通过MR方法筛选出低频趋势项分量,去除这些成分并重构剩余信号,以校正基线漂移。仿真信号分析结果表明,与传统的最小二乘拟合法(ordinary least squares, OLS)和局部均值分解(local mean decomposition, LMD)相比,RLMD方法在提取趋势项方面具有更高的准确性和稳定性,有效避免了模态混叠现象。现场爆破振动监测试验结果显示,与远区振动信号相比,近区实测爆破振动信号受到低频趋势项的干扰更为严重。通过RLMD-MR方法进行基线校正后,信号波形能够有效恢复至基线中心附近,解决了基线漂移问题。
文摘针对传统相位差分法在振动定位中因信噪比不足导致的定位精度低和信号恢复困难问题,提出一种基于相位敏感光时域反射仪(Φ-OTDR)的滑动方差变分模态分解(SV-VMD)振动定位及信号恢复方法。SV算法通过滑动窗口计算相位差分方差,选取振动点前10 m的相位作为修正参考相位,有效抑制累积噪声;VMD通过多尺度分解分离有效信号分量与趋势噪声,消除正交解调引入的直流偏移。实验结果表明:与传统相位差分法相比,SV算法使系统信噪比提升12 d B(从14 d B至26 d B),并在多振动源场景下清晰分辨了400 m和650 m处的干扰事件;经参数优化(惩罚因子α=100,模式数K=3)的VMD算法成功恢复了三角波信号,显著抑制了非振动相关噪声。
文摘针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分解,获取模态分量。接着,将原始信号与这些模态分量分割为多个时间局部片段,并计算它们对应时间局部片段的相关熵值。然后,通过LWT算法处理弱相关分量,最后重构剩余分量以完成去噪过程。实验结果表明:在5 km的传感距离和10 m的空间分辨率的条件下,系统的信噪比达到了54.36 d B,同时均方根误差降低至0.091。
