GitHub - milhamat/Tennessee-Eastman-Process-defect: Dynamic Modeling of Complex Industrial (Tennessee Eastman Process)

Name		Name	Last commit message	Last commit date
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02_Experiment-Look and See the Data.ipynb		02_Experiment-Look and See the Data.ipynb
03_Experiment-PCA for Visualization.ipynb		03_Experiment-PCA for Visualization.ipynb
04_Experiment-PCA.ipynb		04_Experiment-PCA.ipynb
05_Variance and Mean.ipynb		05_Variance and Mean.ipynb
06_Correlation Test.ipynb		06_Correlation Test.ipynb
07_First Apply the Slow Feature Analysis.ipynb		07_First Apply the Slow Feature Analysis.ipynb
08_F-statistic.ipynb		08_F-statistic.ipynb
09_ANOVA Test in TEP dataset_1.ipynb		09_ANOVA Test in TEP dataset_1.ipynb
10_PCA and SFA Multicollinearity Test.ipynb		10_PCA and SFA Multicollinearity Test.ipynb
11_ANOVA Test in TEP dataset_2.ipynb		11_ANOVA Test in TEP dataset_2.ipynb
12_Dimensional Reduction FastICA for Fault Detection with SPE.ipynb		12_Dimensional Reduction FastICA for Fault Detection with SPE.ipynb
13_Dimensional Reduction PCA for Fault Detection with SPE.ipynb		13_Dimensional Reduction PCA for Fault Detection with SPE.ipynb
14_Dimensional Reduction Incremental PCA for Fault Detection with SPE.ipynb		14_Dimensional Reduction Incremental PCA for Fault Detection with SPE.ipynb
15_Dimensional Reduction Kernel PCA for Fault Detection with SPE.ipynb		15_Dimensional Reduction Kernel PCA for Fault Detection with SPE.ipynb
16_Dimensional Reduction NMF for Fault Detection with SPE.ipynb		16_Dimensional Reduction NMF for Fault Detection with SPE.ipynb
17_Dimensional Reduction Truncated SVD for Fault Detection with SPE.ipynb		17_Dimensional Reduction Truncated SVD for Fault Detection with SPE.ipynb
18_Dimensional Reduction Sparse PCA for Fault Detection with SPE.ipynb		18_Dimensional Reduction Sparse PCA for Fault Detection with SPE.ipynb
19_Dimensional Reduction Feature Analysis for Fault Detection with SPE.ipynb		19_Dimensional Reduction Feature Analysis for Fault Detection with SPE.ipynb
20_Dimensional Reduction for Fault Detection.ipynb		20_Dimensional Reduction for Fault Detection.ipynb
21_MSPC with LGB for Fault Detection.ipynb		21_MSPC with LGB for Fault Detection.ipynb
22_MSPC with Gradient Boosting for Fault Detection.ipynb		22_MSPC with Gradient Boosting for Fault Detection.ipynb
23_MSPC with XGBoost for Fault Detection.ipynb		23_MSPC with XGBoost for Fault Detection.ipynb
24_MSPC with Support Vector Regression for Fault Detection.ipynb		24_MSPC with Support Vector Regression for Fault Detection.ipynb
25_MSPC with Polynomial Regression for Fault Detection.ipynb		25_MSPC with Polynomial Regression for Fault Detection.ipynb
26_MSPC with Bayesian Ridge for Fault Detection.ipynb		26_MSPC with Bayesian Ridge for Fault Detection.ipynb
27_MSPC with AdaBoost Regressor for Fault Detection.ipynb		27_MSPC with AdaBoost Regressor for Fault Detection.ipynb
28_MSPC with Bagging Regressor for Fault Detection.ipynb		28_MSPC with Bagging Regressor for Fault Detection.ipynb
29_MSPC with Hist Gradient Boosting Regressor for Fault Detection.ipynb		29_MSPC with Hist Gradient Boosting Regressor for Fault Detection.ipynb
30_comparison performance in dimension reduction for fault detection.ipynb		30_comparison performance in dimension reduction for fault detection.ipynb
31_Understanding more about FDR(Fault Detection Rate).ipynb		31_Understanding more about FDR(Fault Detection Rate).ipynb
32_PCA with T-square .ipynb		32_PCA with T-square .ipynb
32_PCA with T-square_.ipynb		32_PCA with T-square_.ipynb
33_KPCA with T-square.ipynb		33_KPCA with T-square.ipynb
33_KPCA with T-square_.ipynb		33_KPCA with T-square_.ipynb
33_KPCA with T-square_poly.ipynb		33_KPCA with T-square_poly.ipynb
34_SFA with T-square.ipynb		34_SFA with T-square.ipynb
34_SFA with T-square_.ipynb		34_SFA with T-square_.ipynb
E01_PCA hyperparameter n_components.ipynb		E01_PCA hyperparameter n_components.ipynb
E02_fault detection with LGB regression comprehension.ipynb		E02_fault detection with LGB regression comprehension.ipynb
E03_TEP with Scaler technique for NMF.ipynb		E03_TEP with Scaler technique for NMF.ipynb
E04_Manual PCA Inverse Transform.ipynb		E04_Manual PCA Inverse Transform.ipynb
E05_explained_variance-components_ and eigen_val-eigen_vec.ipynb		E05_explained_variance-components_ and eigen_val-eigen_vec.ipynb
E06_pca-kpca eigen value and vector.ipynb		E06_pca-kpca eigen value and vector.ipynb
E07_T2_PCA_(boston).ipynb		E07_T2_PCA_(boston).ipynb
E08_Slow Feature Analysis Delta Values Comparison.ipynb		E08_Slow Feature Analysis Delta Values Comparison.ipynb
E09_Hotelling T square.ipynb		E09_Hotelling T square.ipynb
E10_understand the T square_kpca.ipynb		E10_understand the T square_kpca.ipynb
E10_understand the T square_kpca_experiment.ipynb		E10_understand the T square_kpca_experiment.ipynb
E11_understand the T square_pca.ipynb		E11_understand the T square_pca.ipynb
E12_kpca eigen value and vector.ipynb		E12_kpca eigen value and vector.ipynb
E13_MatToPy_kpca_faultdetection.ipynb		E13_MatToPy_kpca_faultdetection.ipynb
E14_Scaling technique for kpca model.ipynb		E14_Scaling technique for kpca model.ipynb
E15_KPCA-FaultDetection_with different scaling technique.ipynb		E15_KPCA-FaultDetection_with different scaling technique.ipynb
E16_Eigen decompasition manual based on matlab.ipynb		E16_Eigen decompasition manual based on matlab.ipynb
E17_KPCA-FaultDetection_with new eigvals.ipynb		E17_KPCA-FaultDetection_with new eigvals.ipynb
E18_Kernel Function.ipynb		E18_Kernel Function.ipynb
E19_Kernel function implementation into the data.ipynb		E19_Kernel function implementation into the data.ipynb
E20_kpca with matlab data.ipynb		E20_kpca with matlab data.ipynb
E21_kpca with tep data.ipynb		E21_kpca with tep data.ipynb
E22_KPCA_Class.ipynb		E22_KPCA_Class.ipynb
E23_KPCA_Class_with-kernel-func(still_not).ipynb		E23_KPCA_Class_with-kernel-func(still_not).ipynb
E24_Kernel Function try error.ipynb		E24_Kernel Function try error.ipynb
E25_kpca_sklearn_model_try.ipynb		E25_kpca_sklearn_model_try.ipynb
E26_KPCA_Class_Gauss.ipynb		E26_KPCA_Class_Gauss.ipynb
TEP_sampleSize.ipynb		TEP_sampleSize.ipynb
standard_scaler-parameter_setting.ipynb		standard_scaler-parameter_setting.ipynb

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