搜索资源列表
wavelet-in-signal-processing
- 小波变换在信号处理中的应用,包括分解，去噪，以及检测传感器故障等
gear
- 基于db10四层分解的齿轮的故障特征提取-Decomposition based on the four-db10 gear fault feature extraction
基于小波包提取轴承故障
- 基于小波包变换对信号进行分解，提取机器轴承故障的特征信号进行损伤识别，(The signal is decomposed based on wavelet packet transform, and the characteristic signals of machine bearing faults are extracted to identify the damage,)
变压器故障诊断
- EMD分解程序，变压器故障论文，小波分解，各种变压器故障分析论文(EMD decomposition program)
emd
- 基于EMD滚动轴承故障诊断的编程，将振动信号进行分解得到特征频率(Based on EMD programming of rolling bearing fault diagnosis, the vibration signal is decomposed to obtain characteristic frequency)
emd+信息熵
- 可以实现机械EMD经验模态分解，提取特征量并利用神经网络进行模式识别故障类型(The empirical mode decomposition of mechanical EMD can be realized, feature quantity is extracted and neural network is used to identify the type of fault.)
mrsvd
- 实现多分辨率奇异值分解程序,可用于故障诊断、消除噪声等(multi resolvetion svd decompotion)
eemdaddKurtosis criterion
- 利用eemd分解信号以及峭度准则筛选最佳分量，并对筛选的分量做包络谱，进而诊断出故障类型。(EEMD is used to decompose the signal and kurtosis criterion to screen the best components, and the envelope spectrum of the selected components is made, and then the fault types are diagnosed.)
emd+改进阈值函数
- 利用emd分解轴承振动信号，并通过改进阈值函数来筛选IMF分量，最后对分量做包络分析，诊断出故障类型。(Using emd to decompose the bearing vibration signal, the IMF component was selected by improving the threshold function, and finally the component was enveloped and analyzed to diagnose the fault ty
EMD-Hilbert
- 轴承故障诊断中振动信号EMD分解和Hilbert包络(EMD Decomposition and Hilbert Envelope of Vibration Signal in Bearing Fault Diagnosis)
局部经验模态分解
- 基于局部经验模态分解，可以很好地提取轴承故障特征
vmd
- 变分模态分解,用于分解各种信号，可用来故障诊断，特征提取。(Variational mode decomposition, used to decompose various signals, can be used for fault diagnosis and feature extraction.)
VMDtest
- 改进的变分模态分解算法，有效提取振动信号中故障频率。(The improved variational mode decomposition algorithm can effectively extract the fault frequency in vibration signals.)
EMD
- 轴承故障诊断，自己编写的基础EMD分解程序，实测很好用。(Bearing fault diagnosis, self-compiled basic EMD decomposition program, the actual measurement is very useful.)
小波信息熵
- 对信号进行小波变换，确定小波基的选择和分解层数，再求解信号的信息熵，可用于故障诊断。(The wavelet transform is performed on the signal to determine the selection and decomposition layers of the wavelet base, and then the information entropy of the signal is solved, which can be used for fault
变分模态方法
- 变分模态分解方法能够使一个多频带的故障信号，分解出具有单个频带的子信号，然后使用共振解调方法可实现故障信号的诊断。(VMD method can decompose a multi band fault signal into a single band sub signal, and then use resonance demodulation method to realize fault signal diagnosis.)
xiaobo
- 对故障数据的小波包分解与信号重构、小波包能量特征提取 暨 小波包分解后实现按频率大小分布重新排列，并进行降噪处理。(After wavelet packet decomposition and signal reconstruction, wavelet packet energy feature extraction and wavelet packet decomposition, the fault data can be rearranged according to the frequ
小波包能量可视化和GUI设计
- 特色：1.借用小波包分解和小波能量熵函数；2.GUI界面导入西储大学轴承故障数据；3：对提取小波能量方便快捷(Features: 1. Use wavelet packet decomposition and wavelet energy entropy function; 2. GUI interface to import bearing fault data of Xichu University; 3. It is convenient and fast to extract wavel
匹配追踪算法
- 共振稀疏分解常用算法匹配追踪法，用于轴承故障的分离(Resonance sparse decomposition algorithm matching tracking method is commonly used for bearing fault separation)
核主元分析（Kernel principal component analysis ，KPCA）在降维、特征提取以及故障检测中的应用
- 主要功能有： （1）训练数据和测试数据的非线性主元提取（降维、特征提取） （2）SPE和T2统计量及其控制限的计算 （3）故障检测 KPCA的建模过程（故障检测）： （1）获取训练数据（工业过程数据需要进行标准化处理） （2）计算核矩阵 （3）核矩阵中心化 （4）特征值分解 （5）特征向量的标准化处理 （6）主元个数的选取 （7）计算非线性主成分（即降维结果或者特征提取结果） （8）SPE和T2统计量的控制限计算