【Matlab源码】风力发电机故障检测（外文期刊水平仿真）.rar

######################################################################## ###Wind Turbine Fault Detection Using XGBoost, Random Forests and SVM### ######################################################################## Zhejiang Uniersity, Ocean Renewable Energy Lab, Insititute of Ocean Engineering and Technology Yulin. Si Mail:[email protected] Liyang. Qian. Mail:[email protected] ######################################################################## Directories: .../FAST_V8/CertTest -- FAST input files (Read the FAST user's guide before use) .../FAST_V8/Simulink/XGB_TreeModels -- XGBoost dump models .../FAST_V8/Simulink/FaultDetection.mdl -- FD process simulink models (FAST V8 & MATLAB 2015b X86) .../FAST_V8/Simulink/FDIBenchMarkData.m -- Simulation parameters setting .../FAST_V8/Simulink/mat2data.m -- Transfer .mat data to .csv data .../FAST_V8/Simulink/run.m -- Run the simulation (Note to set the path and name of .fst file) .../Python/RF_XGBoost_Training.py -- Training and predicting with RF, XGBoost and SVM (Installed libraries first) .../Python/Dump_XGBoost_Model.py -- Select features with RF and predict using XGBoost, classifier dumped as .txt file ######################################################################## How to observe the FD results: 1)Make sure how to run a FAST-Simulink combined model %确保如何运行一个FAST-Simulink组合模型 2)Set parameters correctly and run 'run.m' %正确设置参数并运行run.m 3)Results in scopes (FaultDetection/Fault Detection Subsystem/...) 范围内的结果 ######################################################################## How to save simulation data, train model and test model: 如何保存仿真数据、训练模型和试验模型: 1)Make sure how to run a FAST-Simulink combined model 确保如何运行一个FAST-Simulink组合模型 2)Set parameters correctly 正确设置参数 3)Change one of the 'Terminator module' to 'To File' module. i.e. FaultDetection/Fault Detection Subsystem/claasification fault 2/Terminator2 3)将“Terminator模块”中的一个改为“to File”模块。即故障检测/故障检测子系统/分类故障2/Terminator2 4)Run 'run.m' and get a .mat file. Name it 'sensordata.mat'. 5)Run 'mat2data.m'. Transfer it to a CSV file. Prepare a training set and a testing set. Name them 'testdata.csv' and 'traindata.csv' 4)Run the 'RF_XGBoost_Training.py' in python 3.6. Note that you need install necessary py library in advance. They are sklearn, pylab, numpy, pandas, xgboost, scipy. 'Dump_XGBoost_Model.py' give a dump file of XGB and you can apply it in simulink model.