岩体在外部荷载冲击作用下会产生不同频率的信号。首先,通过自制探头的光纤监测系统监测现场岩体受到瞬时冲击荷载前后的应力波信号,并采用鲁棒性局部均值分解(robust local mean decomposition,RLMD)方法,结合快速傅里叶变换对实验得...岩体在外部荷载冲击作用下会产生不同频率的信号。首先,通过自制探头的光纤监测系统监测现场岩体受到瞬时冲击荷载前后的应力波信号,并采用鲁棒性局部均值分解(robust local mean decomposition,RLMD)方法,结合快速傅里叶变换对实验得到的监测信号进行时频分析;然后,通过LS-DYNA软件模拟冲击荷载施加于岩体并产生应力波的过程,并将模拟应力波频率与实验监测应力波频率进行对比;最后,分析了弹性模量和密度发生改变时模拟应力波频率的变化。结果表明:在现场施加冲击荷载后,现场监测所得信号经过频谱分解会出现频率为1500~2300 Hz的多个极大振幅特征信号,与模拟应力波时频分析中获得的2203 Hz的主频率信号基本符合;模拟应力波频率与一维平面应力波推导的频率呈相反的变化趋势。展开更多
为有效提取滚动轴承振动信号故障特征,提出了一种基于特征模态分解(Feature Mode Decomposition, FMD)与色散熵(Dispersion Entropy, DisEn)的信号特征提取方法。首先利用FMD方法将不同状态的滚动轴承振动信号分解为若干固有模态函数(In...为有效提取滚动轴承振动信号故障特征,提出了一种基于特征模态分解(Feature Mode Decomposition, FMD)与色散熵(Dispersion Entropy, DisEn)的信号特征提取方法。首先利用FMD方法将不同状态的滚动轴承振动信号分解为若干固有模态函数(Intrinsic Mode Function, IMF)分量;然后计算各IMF分量的DisEn,组合构建原始振动信号的特征向量。仿真实验表明该方法能有效提取滚动轴承振动信号特征,并且根据提取的特征能够较好地识别滚动轴承的故障类型。To effectively extract the vibration signal fault features of rolling bearings, this paper proposed a signal feature extraction method based on Feature Mode Decomposition (FMD) and Dispersion Entropy (DisEn). Firstly, the FMD method is used to decompose the vibration signals of rolling bearings in different states into several Intrinsic Mode Function (IMF) components. Then, the DisEn of each IMF component is calculated and combined to construct the feature vector of the original vibration signal. Simulation experiments show that this method can effectively extract the vibration signal characteristics of rolling bearings, and can identify the fault types of rolling bearings well based on the extracted features.展开更多
文摘岩体在外部荷载冲击作用下会产生不同频率的信号。首先,通过自制探头的光纤监测系统监测现场岩体受到瞬时冲击荷载前后的应力波信号,并采用鲁棒性局部均值分解(robust local mean decomposition,RLMD)方法,结合快速傅里叶变换对实验得到的监测信号进行时频分析;然后,通过LS-DYNA软件模拟冲击荷载施加于岩体并产生应力波的过程,并将模拟应力波频率与实验监测应力波频率进行对比;最后,分析了弹性模量和密度发生改变时模拟应力波频率的变化。结果表明:在现场施加冲击荷载后,现场监测所得信号经过频谱分解会出现频率为1500~2300 Hz的多个极大振幅特征信号,与模拟应力波时频分析中获得的2203 Hz的主频率信号基本符合;模拟应力波频率与一维平面应力波推导的频率呈相反的变化趋势。
文摘为有效提取滚动轴承振动信号故障特征,提出了一种基于特征模态分解(Feature Mode Decomposition, FMD)与色散熵(Dispersion Entropy, DisEn)的信号特征提取方法。首先利用FMD方法将不同状态的滚动轴承振动信号分解为若干固有模态函数(Intrinsic Mode Function, IMF)分量;然后计算各IMF分量的DisEn,组合构建原始振动信号的特征向量。仿真实验表明该方法能有效提取滚动轴承振动信号特征,并且根据提取的特征能够较好地识别滚动轴承的故障类型。To effectively extract the vibration signal fault features of rolling bearings, this paper proposed a signal feature extraction method based on Feature Mode Decomposition (FMD) and Dispersion Entropy (DisEn). Firstly, the FMD method is used to decompose the vibration signals of rolling bearings in different states into several Intrinsic Mode Function (IMF) components. Then, the DisEn of each IMF component is calculated and combined to construct the feature vector of the original vibration signal. Simulation experiments show that this method can effectively extract the vibration signal characteristics of rolling bearings, and can identify the fault types of rolling bearings well based on the extracted features.
