ip_07_10

滚动轴承故障诊断的FFT分析，用故障诊断软件采集的一组数据进行分析(FFT analysis of the rolling bearing fault diagnosis, a set of data collected with the fault diagnosis software to analyze)

Principal component analysis for a voice signal

在目标为1的假设下，应用relax算法进行目标超分辨成像(The assumption that the target was 1, applying relax super-resolution imaging algorithm for target)

说明：  MATLAB潮流计算程序，MATLAB潮流计算程序(Flow calculation program MATLAB, MATLAB flow calculation program)

Volterra 级数，用于非线性建模，其中核需要自己确定，还有记忆长度(Volterra series for nonlinear modeling, in which the core needs to set itself, as well as memory length)

电力系统 重合闸建模，能很好的对分布式电源的重合闸情况进行模拟(Reclosing power system modeling, can be a good situation for reclosing simulation of distributed power)

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初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。( In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value decomposition was proposed for fault diagnosis using standard fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were decomposed by VMD, and the modes characteristics were further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the test samples were clustered by a Hamming nearness approach, and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under variable loads. By comparing with a method based on EMD, this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo)

说明：  test1: a simple example for optical flow: 1 take the camera man picture for example 2 move it with subpixels (interpolate to get the result) 3 optical flow to estimate the motion test2-7 simple examples for picture rotation and translations

Zero-forcing Equalizer

Efficiency optimization of IPMSM with PI controller and loss minimization algorithm. Good for comparison and simulation results.