搜索资源列表
rtcmas_client
- 小波包分析提取振动信号中的特征频率，以及能量谱分析计算-wavelet packet analysis vibration signal from the characteristic frequency, and the energy spectrum analysis
对轴承故障振动信号的matlab小波分析程序
- 对轴承故障振动信号的matlab小波分析程序，能完成对故障特征频率的提取,Of bearing fault vibration signals matlab wavelet analysis procedures, can finish on the fault characteristic frequency extraction
NExT
- 一种适用于多自由度的自然激励技术，可以实现从随机响应信号中提取自由振动信号。-A multi-degree of freedom for the natural excitation technique, can extract from the random response signal free vibration signals.
Wavelet_DecRec
- matlab小波分析，振动信号处理，瞬态信号提取，故障分析-matlab wavelet analysis, signal processing, vibration, transient signal extraction, failure analysis
2005local-mean-decomposition
- 2005年J.S.Smith提出的局域均值分解LMD(Local mean decomposition)的原始文献，该方法可用于故障诊断、特征提取、振动信号分析等多领域，是较EMD更为温和的一种自适应算法，目前是热门研究方法。-This is the original article about LMD——Local mean decompositon. Ihis method is a novel adaptive decomposition. It is more better than E
ARmodelLPCmethods
- 使用Matlab AR模型的LPC函数来做特征值提取。传统方法中LPC一般用来做语音处理。这里每100个数据来做LPC，用获得的N阶参数来作为振动信号的特征值。这样10000个样本可以获得100个特征值样本集。用这些样本集可以做神经网络分类-Matlab AR model LPC function to do eigenvalue extraction. Conventional method LPC generally used for voice processing. Do LPC, wh
RDT
- 随机减量法用于从随机振动信号中提取结构的自由衰减响应信号-Random Decrement Technique （RDT）is used to extract damped free vibration form random vibration
rdt
- 随记减量法是从结构的随机振动响应信号中提取该结构自由衰减振动信号的一种方法-With the mind Reduction Act
xiaobobaonengliangfenxi
- 小波包提取滚动轴承故障能量特征的matlab程序，大家只要把采集的滚动轴承振动信号数据放入程序中即可使用，希望对大家有所帮助。-Bearing Fault wavelet packet energy feature extraction matlab program, we just put a rolling bearing vibration signal collected data into the program to use, we want to help.
xiaobobaoju
- 小波包能量巨程序（已做归一化处理，用于对振动信号提取特征）-Wavelet packet energy giant program (normalization process has been done for the vibration signal feature extraction)
CAL_corner
- 针对结构转角信息难以测量的问题，采用转角信息重构技术由随机子空间从振动信号中提取的结构平动位移估算结构转动位移，然后构建改进的单元损伤变量-Since it is difficult to measure the rotational responses of a structure, a technique was adopted to evaluate the approximate modal rotations with the modal displacements, which we
xiaobobaochengxu
- 小波包降噪、提取能量特征值，用于振动信号处理、故障诊断等。里面有经典的例子，供大家学习-Extract the energy eigenvalues for vibration, wavelet packet de-noising, signal processing, fault diagnosis. There are classic examples, for everyone to learn
zhengdongxinhao
- 对机械类振动信号处理，时域内特征值得提取，均值、均方根、绝对平均幅值、峭度等-For mechanical vibration signal processing, time domain feature extraction worth, mean, RMS, mean absolute amplitude, kurtosis, etc.
Choiwilliams
- 计算振动信号的Choi-Williams分布，通过对照轴承故障特性频率可知，轴承的外圈发现了故障，完成了对外圈的故障特征提取。-The Choi-Williams distribution of the vibration signal is calculated. Through the comparison of the bearing fault characteristic frequency, the outer ring of the bearing is found to be f
finggan_V6.4
- 小波包分析提取振动信号中的特征频率，考虑雨衰 阴影 和多径影响，供做算法研究人员参考。- Wavelet packet analysis to extract vibration signal characteristic frequency, Consider shadow rain attenuation and multipath effects Algorithm for researchers to do reference.
kiuseng
- 利用matlab GUI实现的串口编程例子，对于初学者具有参考意义，小波包分析提取振动信号中的特征频率。- Use serial programming examples matlab GUI implementation, For beginners with a reference value, Wavelet packet analysis to extract vibration signal characteristic frequency.
qaineng_v23
- matlab开发工具箱中的支持向量机，小波包分析提取振动信号中的特征频率，经典的灰度共生矩阵纹理计算方法。- matlab development toolbox support vector machine, Wavelet packet analysis to extract vibration signal characteristic frequency, Classic GLCM texture calculation method.
fangjou
- 可以实现模式识别领域的数据的分类及回归，小波包分析提取振动信号中的特征频率，通过matlab代码。- You can achieve data classification and regression pattern recognition, Wavelet packet analysis to extract vibration signal characteristic frequency, By matlab code.
VMD_test
- 一种分解精度高、模态混叠问题少的VMD特征提取算法，进行振动信号分解,形成初始特征向量矩阵(A VMD feature extraction algorithm with high decomposition accuracy and few modal aliasing problems is used to decompose the vibration signal and form the initial eigenvector matrix.)
基于多信号小波变换分解的特征提取
- 实现对振动信号的小波变换分解的特征提取。(The wavelet transform decomposition of vibration signal is realized to extract its features.)