搜索资源列表
332
- 齿轮箱早期的故障信号往往十分微弱,信噪比低,这大大限制了已有诊断方法在早期诊断中的应用,因此如何获取真实的振动信号是提高齿轮箱早期故障诊断质量的关键,独立分量分析(ICA)为此提供了一种新的思路。文 中研究了ICA在齿轮箱故障早期诊断中的应用,首先分析了齿轮箱的混合振动信号模型,然后针对具体的轴承故障进行了实验,并使用快速ICA算法分离出轴承的振动信号-The early gearbox fault signal is often very weak, low signal-to-noise
lunwen
- 最新关于故障诊断的论文，有LMD算法，还有EMD算法，主要是做轴承磨损预测的-failed to translatefailed to translatefailed to translatefailed to translate
81
- 滚动轴承是各种机电设备中的重要部件，其主要特点是其寿命的随机性较大，且它的好坏直接影响到设备的正常运行。因而掌握轴承运行的工作状态以及故障的形成和发展是目前机械故障诊断领域中研究的重要内容之一。利用轴承的随机振动信号对其工作状态进行诊断是目前最常用的方法-Rolling is a variety of mechanical and electrical equipment is an important component, its main feature is its randomness
84
- 滚动轴承故障诊断是机械故障检测中一个重要方面。使用小波包分析和包络分析相结合的方法提取轴承微弱振动信号, 克服了传统包络分析方法易丢失信号有效成分的缺点。包络信号的细化谱较好体现了轴承故障信息。-Bearing Fault Diagnosis of mechanical fault detection in an important aspect. The use of wavelet packet analysis and envelope analysis method of combini
envelope-bearing-diagnosis
- 共振解调法轴承诊断 包络谱包括外环内环滚子故障诊断-Resonance demodulation method envelope bearing diagnostic spectrum, including outer inner roller Troubleshooting
Wind-power-bearing-test
- 风电机组轴承的状态监测和故障诊断与运行维护-Wind power bearing test rig monitoring and data acquisition system research
Wind-power-bearing-test
- 风电机组轴承的状态监测和故障诊断与运行维护-Wind turbine bearing condition monitoring and fault diagnosis and operation and maintenance
111
- 轴承内圈的故障诊断数据来自于美国西科大学，可以用于外圈，滚动体的故障诊断-The inner ring of the bearing fault diagnosis data from USA CK University, can be used for fault diagnosis of the outer ring, rolling body
b753937fc8d5
- Matlab轴承外环故障诊断以及应用原理-Matlab bearing outer ring and the application of the principle of fault diagnosis
tqwt_matlab_toolbox
- 可调Q因子小波变换，用在故障诊断中，程序中包含了原始TQWT工具箱和轴承振动信号信号的谱包络的求取等，最新的应用成果-Adjustable Q-factor wavelet transform used in fault diagnosis, the program includes a spectral envelope of the original TQWT toolbox and bearing vibration signal signal to strike, etc., the l
bearing-envelope-analysis-
- 对轴承振动信号进行滤波和包络频谱分析，可通过包络谱发现轴承故障，也可用于其他故障诊断的研究，程序已调试通过-Bearing vibration signal filtering and envelope spectrum analysis, bearing failure can be found through the envelope spectrum can also be used to study other troubleshooting procedures have been d
lly1
- 针对滚动轴承故障信号具有非平稳、非高斯的特点，提出了将时域分析与小波分析相结合的方法对滚动轴承进行故障诊断。在研究不同信号分析方法理论的基础上，以滚动轴承外圈故障振动信号为例，采用多种信号处理方法进行了分析。结果表明，各种分析方法在分析轴承故障时的特点各不相同，在实际使用中，可将时域分析与小波分析综合使用，实现轴承状态的实时监测与故障的准确定位。-For rolling bearing fault signals have non-stationary, non-Gaussian, we pro
code_for
- 针对机械行业变转速零件如轴承，齿轮箱的故障诊断，采用阶次分析技术。程序针对变转速工况下的故障诊断。-Variable speed machinery industry parts such as bearings, gearboxes for fault diagnosis using order analysis techniques. Procedures for troubleshooting variable speed conditions.
fault_diagnios
- 基于JADE和ELM的轴承故障程度跟踪，分别对轴承的外圈，内圈和滚子进行诊断，并和其他方法进行对比-Tracking based on JADE and ELM bearing fault degree, respectively, of bearing outer ring, inner ring and roller diagnosis, and were compared with other methods based on JADE and ELM bearing fault degr
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, the data is bearing data
wodechengxudisange
- 有关轴承的故障诊断算法的程序，需要的同学可以-Bearing fault diagnosis algorithm related programs, students need to look
AAA
- 轴承故障信号的诊断方法，时延相关预处理对振动信号做预处理，可以起到解调的作用-Diagnosis bearing fault signal delay associated pretreatment on the vibration signal preprocessing, can play a role demodulated
yhfb
- Morlet小波对滚动轴承信号进行分析，对形状参数优化，以诊断出轴承故障-Morlet wavelet Rolling signal analysis, the shape parameter optimization, in order to diagnose bearing faults
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
EMDandBP
- 结合emd和bp神经网络的算法, 对轴承故障数据进行诊断,数据可以自己添加(EMD and BP algorism are combined to classify different kinds of faults of bearings. Source data need to be added by yourself)