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基于LabVIEW的过控实验系统(本科毕设)
This is the classification of bearing data-set provided by CWRU.
Digital Signal Processing
This is a MATLAB project which can calculate EMD/EEMD
PhD research-related software on fault diagnosis
Fault Diagnosis with Machine Learning Methods, the dissertation project of my MSc Data Science degree at King's College London
Bearing fault diagnosis is important in condition monitoring of any rotating machine. Early fault detection in machinery can save millions of dollars in emergency maintenance cost. Different techniques are used for fault analysis such as short time Fourier transforms (STFT), Wavelet analysis (WA), cepstrum analysis, Model based analysis, etc. we have doing detecting bearing faults using FFT and by using Wavelet analysis more specifically wavelet Analysis up to two levels of approximations and detail components. The analysis is carried out offline in MATLAB. Diagnosing the faults before in hand can save the millions of dollars of industry and can save the time as well. It has been found that Condition monitoring of rolling element bearings has enabled cost saving of over 50% as compared with the old traditional methods. The most common method of monitoring the condition of rolling element bearing is by using vibration signal analysis. Measure the vibrations of machine recorded by velocity
利用PSO优化的SVM进行期货预测
基本遗传算法MATLAB程序
Machine-learning: Random Forests, SVM, Incremental Learning
Matlab code for multiclass classification using SVM for fault detection
Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)
There are 4 classes of faults arising in the wind turbine due to fault in the bearings. The data acquired is analysed using various machine learning algorithms. Since, it's the case of multi class classification, the data is analysed using Multi class Support Vector Machines (SVM), Discriminant Analysis, Random Forests and K-Nearest Neighbours (KNN)
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