def all_features_li_2019_classifier_ffnn_rul_prediction(): training_set = read_dfs(LEARNING_SET, ALL_FEATURES) # Two-Stage: lei et al 2019 training_set, first_prediction_times = cut_fpts(training_set) training_set = procentual_rul(training_set, first_prediction_times) # Visualize FPTs plot_fpts(first_prediction_times, training_set, 'fourier_entropy') # Concatenate trainings data concatenated_training_set = pd.concat( training_set, ignore_index=True, keys=['Bearing' + str(x) for x in range(0, len(training_set))]) labels = concatenated_training_set.pop('RUL') # Train FFNN trainings_history, ffnn = fit_ffnn(X=concatenated_training_set, y=labels, dropout=True, epochs=50) # Visualize training history and later validation history plot_trainings_history(trainings_history) # Visualize predicted RUL in comparison to real RUL of learning set plot_rul_comparisons(training_set, prediction_model=ffnn) # TESTING ESTIMATIONS # testing_set = read_dfs(FULL_TEST_SET, ALL_FEATURES) testing_set, fpts = cut_fpts(testing_set) testing_set = procentual_rul(testing_set, fpts) plot_rul_comparisons(testing_set, prediction_model=ffnn)
def ahmad_health_indicator_ahmad_classifier_ffnn_rul_prediction(): training_set = read_feature_dfs(LEARNING_SET) training_labels = pop_labels(training_set) training_set = construct_ahmad_health_indicator(training_set) training_set = [ pd.merge(training_set[i], training_labels[i], left_index=True, right_index=True) for i in range(len(training_labels)) ] cut_dfs, fpts = cut_fpts(training_set, fpt_method=ahmad_et_al_2019, signal_key='ahmad_health_indicator') plot_fpts(fpts, df_list=training_set, classification_indicator='ahmad_health_indicator') training_set = concat_dfs(training_set) training_labels = training_set.pop('RUL') ffnn, trainin_history = fit_ffnn(training_set, training_labels, dropout=True, epochs=30, hidden_layers=3, hidden_units=128) comparison_set = read_feature_dfs(FULL_TEST_SET) comparison_set, first_prediction_times = cut_fpts(comparison_set) # Remove label label_data = pop_labels(comparison_set) plot_rul_comparisons(comparison_set, label_data=label_data, prediction_model=ffnn)
def tba_features_ahmad_et_al_2019_ffnn(): feature_list = BASIC_STATISTICAL_FEATURES fpt_method = ahmad_et_al_2019 signal_key = "root_mean_square" learning_set = read_feature_dfs(data_set_sub_set=LEARNING_SET, features=feature_list) # Calculate FPTs and cut dataframes cut_learning_set, first_prediction_times = cut_fpts(learning_set, fpt_method=fpt_method, signal_key=signal_key) plot_fpts(first_prediction_times=first_prediction_times, df_list=learning_set, classification_indicator="root_mean_square") cut_learning_set = concat_dfs(cut_learning_set) training_labes = cut_learning_set.pop('RUL') ffnn, trainings_history = fit_ffnn(X=cut_learning_set, y=training_labes, epochs=100) # Plot trainings history plot_trainings_history(trainings_history=trainings_history, error_type='RMSE') # Read test set testing_set = read_feature_dfs(data_set_sub_set=FULL_TEST_SET, features=feature_list) cut_testing_set, _ = cut_fpts(testing_set, fpt_method=fpt_method, signal_key=signal_key) testing_labels = pop_labels(cut_testing_set) plot_rul_comparisons(cut_testing_set, testing_labels, ffnn)
def all_features_and_autoencoder_li_2019_classifier_ffnn_rul_prediction(): # Input features: statistical features learning_feature_df_list = read_feature_dfs(LEARNING_SET, FEATURES_CSV_NAME) # Two-Stage: lei et al 2019 cut_dfs, first_prediction_times = cut_fpts(learning_feature_df_list) # Visualize FPTs # plot_fpts(first_prediction_times, learning_feature_df_list, 'root_mean_square') # Concatenate trainings data all_bearings = concat_dfs(cut_dfs) labels = all_bearings.pop('RUL') all_bearings, pca = pca_embedded_data_frame(all_bearings) # RUL prediction: FFNN trainings_history, ffnn = fit_ffnn(X=all_bearings, y=labels, dropout=True, epochs=150) # Visualize training history and later validation history plot_trainings_history(trainings_history) # Visualize predicted RUL in comparison to real RUL comparison_set = read_feature_dfs(FULL_TEST_SET, FEATURES_CSV_NAME) comparison_set, first_prediction_times = cut_fpts(comparison_set) # Remove label label_data = pop_labels(comparison_set) # Apply PCA comparison_set = [pd.DataFrame(pca.transform(df)) for df in comparison_set] plot_rul_comparisons(comparison_set, label_data=label_data, prediction_model=ffnn)
def tba_features_li_et_al_2019_svr(): feature_list = [] use_hs_classifier = True fpt_method = li_et_al_2019 signal_key = "kurtosis" learning_set = read_feature_dfs(data_set_sub_set=LEARNING_SET, features=feature_list) if use_hs_classifier: # Calculate FPTs and cut dataframes cut_learning_set, first_prediction_times = cut_fpts( learning_set, fpt_method=fpt_method, signal_key=signal_key) plot_fpts(first_prediction_times=first_prediction_times, df_list=learning_set, classification_indicator="root_mean_square") cut_learning_set = concat_dfs(cut_learning_set) training_labes = cut_learning_set.pop('RUL') svr, trainings_history = fit_svr(X=cut_learning_set, y=training_labes) else: learning_set = concat_dfs(learning_set) training_labes = learning_set.pop('RUL') svr, trainings_history = fit_svr(X=learning_set, y=training_labes) # Plot trainings history plot_trainings_history(trainings_history=trainings_history, error_type='RMSE') # Read test set testing_set = read_feature_dfs(data_set_sub_set=FULL_TEST_SET, features=feature_list) if use_hs_classifier: testing_set, _ = cut_fpts(testing_set, fpt_method=fpt_method, signal_key=signal_key) testing_labels = pop_labels(testing_set) plot_rul_comparisons(testing_set, testing_labels, svr)
def statistical_features_li_2019_classifier_ffnn_rul_prediction(): # Input features: statistical features learning_feature_df_list = read_dfs(LEARNING_SET, FEATURES_CSV_NAME, features=BASIC_STATISTICAL_FEATURES) # Two-Stage: lei et al 2019 cut_dfs, first_prediction_times = cut_fpts(learning_feature_df_list) # Visualize FPTs # plot_fpts(first_prediction_times, learning_feature_df_list, 'root_mean_square') # Concatenate trainings data all_bearings = concat_dfs(cut_dfs) labels = all_bearings.pop('RUL') # RUL prediction: FFNN trainings_history, ffnn = fit_ffnn(X=all_bearings, y=labels, dropout=True, epochs=100, hidden_layers=3, hidden_units=1024) # Visualize training history and later validation history plot_trainings_history(trainings_history) # Visualize predicted RUL in comparison to real RUL on trainings set label_data = pop_labels(cut_dfs) plot_rul_comparisons(cut_dfs, label_data=label_data, prediction_model=ffnn) # Visualize predicted RUL in comparison to real RUL on test set comparison_set = read_dfs(FULL_TEST_SET, FEATURES_CSV_NAME, features=BASIC_STATISTICAL_FEATURES) comparison_set, first_prediction_times = cut_fpts(comparison_set) # Remove label label_data = pop_labels(comparison_set) plot_rul_comparisons(comparison_set, label_data=label_data, prediction_model=ffnn)
def tba_features_li_et_al_2019_ffnn(): feature_list = [] fpt_method = li_et_al_2019 signal_key = "h_kurtosis" learning_set = read_feature_dfs_as_dict(data_set_sub_set=LEARNING_SET) # Calculate FPTs and cut dataframes cut_learning_set, first_prediction_times = cut_fpts(learning_set, fpt_method=fpt_method, signal_key=signal_key) plot_fpts(first_prediction_times=first_prediction_times, df_dict=learning_set, classification_indicator="h_kurtosis")