搜索资源列表
xiaobofenjie
- 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
1
- 轴承全寿命数据的各种时域频域特征提取，包括17个时域和13个频域特征-Bearing a variety of life-cycle data in time domain frequency domain feature extraction
xiaobobao-BP-zhoucheng-zhenduan-
- 基于小波包特征向量与神经网络的滚动轴承故障诊断。：基于故障轴承的特征提取，提出了将小波包分析与神经网络结合的滚动轴承故障诊断方法。对滚动轴承信号进行3层小波包分解，构造小波包特征向量作为故障样本，用训练好的BP神经网络进行故障诊断，试验结果表明，该方法能够有效地诊断出滚动轴承的故障类型。-Fault Diagnosis of Rolling Bearings Based on W avelet Packet Energy Eigen
program-and-data-sets
- 本程序是用于在时域中分析处理轴承故障数据的，提取了各种时域的特征参数，包括均值，有效值，峭度，裕度指标，波形指标等等 适合初学者用于轴承的故障诊断中对参数的提取。（附上轴承故障数据3类共21组 正常、内圈、外圈）-This procedure is used in the time domain analysis of bearing fault data processing to extract the characteristic
bearing
- 轴承信号故障诊断小波特征提取 采用公开的轴承信号作为源信号-Bearing fault diagnosis signal wavelet feature extraction
tuxiangfenxi
- 在matlab中对大量的轴承故障的特征信号进行小波包分解，提取特征值，并建立神经网络训练，最终可由网络进行故障的分析-In matlab large number of bearing fault characteristic signal wavelet packet decomposition, extract characteristic values, and the establishment of neural networ
med2d
- 一种基于最小熵反褶积( ]8+83)3 6+D*(A4 Q20(+W(-)D8(+，]6Q) 的滚动轴承故障特征提取方法: 在利用 /X 模型去除齿轮啮合产生的确定性信号的基础上，对保留信号进行最小熵反褶积，增强冲击信号"该方法避免了传统轴承故障诊断方法中带通滤波器设计的难题，实车测试表明: 与共振解调技术相比，该方法提取的滚动轴承故障特征更加明显，更适合于工程应用"-Based on minimum entropy deconvolu
zhuchengxu
- 读取轴承全寿命周期中的每组数据，并对其进行多域特征提取，包括时域和频域特征提取，用于轴承性能退化评估的研究很有用，自己亲手调试成功-Read bearing the full life cycle of each set of data, and extract its multi-domain characteristics, including time domain and frequency domain feature ext
WAVPENE
- 小波包分析结合能量算法实现特征信号的提取，其中还包含用于测试的轴承数据-Wavelet packet analysis combined with the energy of signal feature extraction algorithm, which also contains the data for bearing test
time-frequency-feature
- 此代码用于故障诊断特征提取，所提取特征包括传统时、频域特征和时频特征三部分，数据为轴承数据-This code is used when troubleshooting feature extraction, the extracted features, including traditional, frequency domain and time-frequency characteristics of three parts,
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 o
simulation-of-planetar1
- 变速器齿裂及轴承故障建模与故障特征提取, -Failure path based modified gear mesh stiffness for spur gear pair with tooth root crack
Parameter-optimization
- 针对滚动轴承早期故障特征提取困难的问题，提出一种基于参数优化变分模态分解的轴承早期故障诊断方法。首先利用粒子群优化算法对变分模态分解算法的最佳影响参数组合进行搜索，搜索结束后根据所得结果设定变分模态分解算法的惩罚参数和分量个数，并利用参数优化变分模态分解算法对故障信号进行处理。-Aiming at the difficult problem of early fault feature extraction of rolling bea
pinyutezheng
- 一维信号频域特征提取，可用于轴承故障诊断和趋势预测-Wherein the one-dimensional frequency domain signal extraction, can be used to predict bearing Fault Diagnosis and
RSSD
- 该代码针对滚动轴承故障振动信号呈现出非线性、非平稳性及噪声背景较强等特点，为了有效提取故障特征，使用的一种共振稀疏分解（Resonance-based sparse signal decomposition，RSSD）与小波变换相结合的振动信号特征提取技术的相关仿真实验程序和轴承数据分解程序。其中，共振稀疏分解是基于品质因子可调小波变换与形态分量分析的一种新的信号分解方法，与常规的基于频带划分的信号分解方法不同，它依据信号各分量的振荡形
p_wavelet
- 小波分解轴承故障诊断代码，包括特征提取，用的数据是西储大学轴承数据(Wavelet decomposition bearing fault diagnosis code, including feature extraction, the data used is the West Park University bearing data)
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,)
轴承特征提取
- matlab代码，轴承特征提取，包括rms、最大幅值、峰值因子、绝对平均值、平均值、峭度、脉冲因子、裕度因子、能量，小波包、经典模态分解等特征(Matlab code, bearing feature extraction, including rms, maximum amplitude, crest factor, absolute mean, mean, kurtosis, pulse factor, margin factor,
时域特征和频域特征
- MATLAB的轴承故障时域特征和频域特征提取(Bearing fault time domain feature and frequency domain feature extraction)
matlab
- 滚动轴承的时域统计特征matlab程序，包括均方根值、方根幅值、峭度等量纲指标(The rolling bearing is diagnosed and the fault features are extracted by using the data of Western storage.)