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ALabVIEW
- 轴承故障判断的好帮手，非常好的程序，适合程序员开发-Bearing fault diagnosis is a good helper, a very good program for programmers to develop
bearingcodes1
- bearing fault matlab codes
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- The Fault Diagnosis of the Rolling Bearing Based on the LMD and Time-frequency Analysis
diagnosis--based-on-LEM
- A novel fault feature extraction method based on the local mean decomposition technology and multi-scale entropy is proposed in this paper. When fault occurs in roller bearings, the vibration signals picked up would exactly display non-stationary
xiaoboshenjingwangluo
- 提出了采用小波包的方法对供暖双吸式离心水泵轴承振动信号进行去噪和提取表征 相应轴承故障的频带能量 并采用 BP 神经网络进行训练和故障识别 通过 MATLAB 进行了仿真经试验验证该方法能够有效地识别出轴承故障-The wavelet package is adoptted to De-noise and extract band energy that represent bearing fault. and the BP neural network is adopting to t
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- 滚动轴承故障诊断的阶比多尺度形态学解调方法_徐亚军-Order Bearing Fault Diagnosis than multiscale morphology demodulation method _ XU Ya-jun
BearingAnalysis
- bearing fault toolbox
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
基于VMD和Teager能量谱的滚动轴承故障特征提取
- 基于VMD和1_5维Teager能量谱的滚动轴承故障特征提取_向玲(To extract _ Ling fault features of rolling bearing VMD and 1_5 dimension Teager based on energy spectrum)
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- 很好的深度学习资料，用深度学习dbn模型在这个轴承故障的分类上一个很好的应用(Using this deep learning DBN model in bearing fault classification)