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 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 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 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 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 train(self, training_data: pd.DataFrame, training_labels: pd.Series,
validation_data: pd.DataFrame,
validation_labels: pd.Series) -> History:
training_data = training_data[self.feature_list]
validation_data = validation_data[self.feature_list]
self.prediction_model, self.trainings_history = fit_ffnn(
X=training_data,
y=training_labels,
dropout=True,
validation_data=(validation_data, validation_labels))
return self.trainings_history
def train(self, training_data: pd.DataFrame, training_labels: pd.Series,
validation_data: pd.DataFrame,
validation_labels: pd.Series) -> History:
training_data = self._fit_and_embed_training_data(training_data)
scaler = MinMaxScaler()
training_data = pd.DataFrame(scaler.fit_transform(training_data),
columns=training_data.columns)
if validation_data is not None:
validation_data = self._embed_data(validation_data)
if validation_data is not None:
self.prediction_model, self.trainings_history = fit_ffnn(
X=training_data,
y=training_labels,
dropout=True,
validation_data=(validation_data, validation_labels))
else:
self.prediction_model, self.trainings_history = fit_ffnn(
X=training_data,
y=training_labels,
dropout=True,
validation_data=None)
return self.trainings_history
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 entropy_features_no_classifier_ffnn():
feature_list = ENTROPY_FEATURES
learning_set = read_feature_dfs(data_set_sub_set=LEARNING_SET,
features=feature_list)
learning_set = concat_dfs(learning_set)
training_labes = learning_set.pop('RUL')
ffnn, trainings_history = fit_ffnn(X=learning_set,
y=training_labes,
epochs=100)
plot_trainings_history(trainings_history=trainings_history,
error_type='RMSE')
testing_set = read_feature_dfs(data_set_sub_set=FULL_TEST_SET,
features=feature_list)
testing_labels = pop_labels(testing_set)
plot_rul_comparisons(testing_set, testing_labels, 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)
