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 isomap_features_no_classifier_ffnn_rul_prediction(training_data,
comparison_set):
# Read Training data
isomap_training_data = training_data
# Remove labels
isomap_training_data = concat_dfs(isomap_training_data)
labels = isomap_training_data.pop('RUL')
model_training_data, isomap = isomap_embedded_data_frame(
isomap_training_data, verbose=False)
ffnn, training_history = fit_ffnn(model_training_data, labels, epochs=100)
plot_trainings_history(training_history)
# Visualize predicted RUL in comparison to real RUL
# comparison_set = read_raw_dfs(FULL_TEST_SET)
# Remove label
label_data = pop_labels(comparison_set)
# Apply autoencoder
comparison_set = [
pd.DataFrame(isomap.transform(X=df)) for df in comparison_set
]
plot_rul_comparisons(comparison_set,
label_data=label_data,
prediction_model=ffnn)
def all_features_pca_combination_no_classifier_ffnn_rul_prediction():
# Read Training data
feature_list = ALL_FEATURES
training_data = read_feature_dfs(data_set_sub_set=LEARNING_SET, features=feature_list)
learning_set = concat_dfs(training_data)
training_labes = learning_set.pop('RUL')
# Remove labels
model_training_data, pca = pca_embedded_data_frame(learning_set, verbose=False)
model_training_data = pd.DataFrame(model_training_data)
ffnn, training_history = fit_ffnn(model_training_data, training_labes, epochs=60) # TODO dropout=True
plot_trainings_history(training_history)
# Visualize predicted RUL in comparison to real RUL of training set
# Remove label
training_label_data = pop_labels(training_data)
# Apply PCA
transformed_training_set = []
for df in tqdm(training_data, desc="Transforming validation set.", position=0, leave=True):
transformed_training_set += [pd.DataFrame(pca.transform(X=df))]
plot_rul_comparisons(transformed_training_set, label_data=training_label_data, prediction_model=ffnn)
# Visualize predicted RUL in comparison to real RUL of validation set
comparison_set = read_feature_dfs(data_set_sub_set=FULL_TEST_SET, features=feature_list)
# Remove label
label_data = pop_labels(comparison_set)
# Apply PCA
transformed_comparison_set = []
for df in tqdm(comparison_set, desc="Transforming validation set.", position=0, leave=True):
transformed_comparison_set += [pd.DataFrame(pca.transform(X=df))]
plot_rul_comparisons(transformed_comparison_set, label_data=label_data, prediction_model=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 autoencoder_features_no_classifier_ffnn_rul_prediction():
"""
VERY VERY Bad -> autoencoder hyperparams?
:return: Void
"""
# Read Training data
autoencoder_training_data = read_raw_dfs(sub_set=LEARNING_SET)
# Remove labels
autoencoder_training_data = concat_dfs(autoencoder_training_data)
labels = autoencoder_training_data.pop('RUL')
autoencoder, encoder = fit_autoencoder(autoencoder_training_data,
epochs=100,
encoding_size=80)
def statistical_frequency_features_no_classifier_ffnn_rul_prediction():
trainings_data = read_feature_dfs(LEARNING_SET)
trainings_data = concat_dfs(trainings_data)
trainings_labels = trainings_data.pop('RUL')
filtered_cols: list = [
column for column in trainings_data if column.startswith('frequency_')
]
trainings_data = trainings_data[filtered_cols]
ffnn, trainings_history = fit_ffnn(trainings_data,
trainings_labels,
dropout=True,
epochs=100,
hidden_units=1024,
hidden_layers=4)
# Visualize trainings history
plot_trainings_history(trainings_history)
def pca_features_no_classifier_ffnn_rul_prediction(training_data,
comparison_set):
hi = __name__
# Read Training data
# Remove labels
pca_training_data = concat_dfs(training_data)
labels = pca_training_data.pop('RUL')
model_training_data, pca = pca_embedded_data_frame(pca_training_data,
n_components=300,
verbose=False)
model_training_data = pd.DataFrame(model_training_data)
ffnn, training_history = fit_ffnn(model_training_data,
labels,
epochs=60,
dropout=True)
plot_trainings_history(training_history)
# Visualize predicted RUL in comparison to real RUL of training set
# Remove label
training_label_data = pop_labels(training_data)
# Apply PCA
transformed_training_set = []
for df in tqdm(training_data,
desc="Transforming training set.",
position=0,
leave=True):
transformed_training_set += [pd.DataFrame(pca.transform(X=df))]
plot_rul_comparisons(transformed_training_set,
label_data=training_label_data,
prediction_model=ffnn)
# Visualize predicted RUL in comparison to real RUL of validation set
# Remove label
label_data = pop_labels(comparison_set)
# Apply PCA
transformed_comparison_set = []
for df in tqdm(comparison_set,
desc="Transforming validation set.",
position=0,
leave=True):
transformed_comparison_set += [pd.DataFrame(pca.transform(X=df))]
plot_rul_comparisons(transformed_comparison_set,
label_data=label_data,
prediction_model=ffnn)
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 df_dict_to_df_dataframe(
df_dict: Dict[str, pd.DataFrame]) -> (pd.DataFrame, pd.Series):
data = concat_dfs(df_dict)
labels = data.pop("RUL")
return data, labels
