搜索资源列表
BearingAnalysis
- bearing fault toolbox
cluster_VMDaFCM_casedat
- 为了精准、稳定地提取滚动轴承故障特征,提出了基于变分模态分解和奇异值分解的特征提取方法,采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识 别。对同一负荷下的已知故障信号进行变分模态分解,利用 奇异值分解技术进一步提取各模态特征,通过FCM形成标准聚类中心,采用海明贴近度对测试样本进行分类,并通过计算分类系数和“卜均模糊嫡对分类性能进行评价,将该方法 应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比,该方法对标准FCM
harmonic
- 该功能主要是计算harmoonic小波变换的旋转设备状态监测的基于振动的轴承故障诊断。-The function basically is for computing Harmoonic wavelet transform for condition Monitoring of rotating equipments by vibration based bearing fault diagnosis.
mckd
- Mckd用于提取周期信号中的冲击成分,亲测可用,不喜勿喷- MAXIMUM CORRELATED KURTOSIS DECONVOLTUION code and method by Geoff McDonald (glmcdona@gmail.com), May 2011 This code file is an external reference for a paper being submitted for review.
kurtogram-method
- 关于一篇英文论文中峭度谱分析方法的变成,用于轴承故障诊断中。-A paper on a English kurtosis spectrum into analysis method, used for fault diagnosis of bearing.
failure-diagnosis-method
- 文章描述的是EEMD、Renyi熵和SVM结合的滚动轴承的故障诊断过程。-The article describes the EEMD, Renyi entropy and SVM combination of rolling bearing fault diagnosis process
gongzhenjiantiao
- 用于对于轴承振动信号的共振解调程序,便于对轴承故障信号进行分析-Used for resonant demodulation of bearing vibration signals for easy analysis of bearing fault signals
VMD
- 本文介绍了一种自适应信号分解新方法-变分模态分解,并且针对滚动轴承早期故障识别困难这一问题,提出了基于VMD的诊断方法。-In this paper, a new adaptive signal decomposition method, variational mode decomposition, is introduced. Aiming at the problem of early fault identification of rolling bearing, a diagnosis
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 bearing, an early fault diagnosis
VMD-Parameter-Estimation
- 变分模态分解在信号分解精度和噪声鲁棒性方面具有明显优势,但需预先确定模态数K,而目前K 只能靠先验知识进行预估,如果预估的K 与实际信号存在差异,会导致分解误差较大。针对以上问题,利用EMD 不需预先设定模态数的自适应分解特点,通过对EMD 分解结果的分析,进行VMD 分解模态数的估计,并通过仿真信号分析及滚动轴承故障信息提取-Variational modal decomposition has obvious advantages in signal decomposition accura
EEMD
- 信号处理,轴承故障,matlab,EMD,EEMD,故障,经验模态分解-Signal processing,Bearing fault,emd,eemd
lmd
- 故障处理,信号处理,轴承故障,LMD,EMD-Fault processing, signal processing, bearing failure, LMD, EMD
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态不同对信号进行分解。 同时还提供了可调谐 Q 因子小波
VMD
- 可以实现滚动轴承的故障采集处理,变分模态分解法很强大(Rolling bearing fault acquisition and processing can be realized, and the variational modal decomposition method is very powerful)
轴承故障频率计算器V3.0_2017.5.23
- 对于轴承故障频率的计算,皮肤界面的修改,相应的计算器软件算法(Calculation of bearing fault frequency)
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)
motor emd
- 小波变换及经验模式分解方法在电机轴承早期故障诊断中的应用(Study on the method of incipient motor bearing fault diagnosis based on wavelet transform and EMD)
故障频率计算
- 轴承故障特征频率计算程序,用于计算轴承存在缺陷时,计算轴承故障特征频率(Bearing fault characteristic frequency calculation program, used to calculate the bearing defects, calculate the bearing fault characteristic frequency)
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)