def longest_sample_RUL_prediction():
for sub_dataset in ['FD001','FD002','FD003','FD004']:
res_rmse = pd.read_csv(os.path.join(results_dir, sub_dataset, sub_dataset+'.csv'))
best_model_name = 'model_'+str(res_rmse.iloc[res_rmse[' RMSE'].idxmin(),0])+'_results.npz'
res_data = np.load(os.path.join(results_dir, sub_dataset, best_model_name),allow_pickle=True)
y_true = res_data['y_true']
y_pred = res_data['y_pred']
md_data = np.load(os.path.join('processed_data', 'CMAPSS_md_dataset.npz'), allow_pickle=True)[sub_dataset][()]
x_md = md_data['x_test_md']
x_ds = md_data['test_deg']
test_lens = [len(x) for i, x in enumerate(x_md) if x_ds[i] < len(x) -1]
max_len_sample_ind = np.argwhere(test_lens == np.max(test_lens))[0][0]
sample_true = y_true[max_len_sample_ind]
sample_pred = y_pred[max_len_sample_ind]
sample_rmse = rmse_eval(sample_true, sample_pred)
plt.figure()
plt.plot(np.arange(1, len(sample_true)+1,1), sample_true[::-1], label='True RUL')
plt.plot(np.arange(1, len(sample_true)+1,1), sample_pred[::-1], 'r', label='Predicted RUL')
plt.xlabel('Operational cycles since degradation start detection')
plt.ylabel('Remaining Useful Life')
plt.legend()
plt.text(0.65, 0.7, 'Sample RMSE '+'{:.2f}'.format(sample_rmse),
fontsize=10, bbox=dict(facecolor='green', alpha=0.3), transform=plt.gca().transAxes)
plt.savefig(os.path.join('plots','RUL_results',sub_dataset+'_longest_sample_RUL.png'),
bbox_inches='tight', pad_inches=0)
def FD004_cases_RUL():
res_rmse = pd.read_csv(os.path.join(results_dir, 'FD004', 'FD004.csv'))
best_model_name = 'model_'+str(res_rmse.iloc[res_rmse[' RMSE'].idxmin(),0])+'_results.npz'
res_data = np.load(os.path.join(results_dir, 'FD004', best_model_name),allow_pickle=True)
y_true = res_data['y_true']
y_pred = res_data['y_pred']
md_data = np.load(os.path.join('processed_data', 'CMAPSS_md_dataset.npz'), allow_pickle=True)['FD004'][()]
x_md = md_data['x_test_md']
x_ds = md_data['test_deg']
threshold = md_data['threshold']
md_ind = [i for i, x in enumerate(x_md) if x_ds[i] < len(x) -1]
indexes = []
# Longest RUL sample
test_lens = [len(y) for y in y_pred]
indexes.append(np.argwhere(test_lens == np.max(test_lens))[0][0])
# Random regeneration
pred_ruls = [np.min(y[150:]) if len(y)>150 else np.inf for y in y_pred]
indexes.append(np.argwhere(pred_ruls == np.min(pred_ruls))[0][0])
for ind in indexes:
sample_true = y_true[ind]
sample_pred = y_pred[ind]
sample_rmse = rmse_eval(sample_true, sample_pred)
md_sample = x_md[md_ind[ind]]
no_deg_sample = md_sample[:x_ds[md_ind[ind]]]
deg_sample = md_sample[x_ds[md_ind[ind]]:]
fig, ax = plt.subplots(2,1, figsize=(7,7))
ax.flat
ax[0].plot(x_ds[md_ind[ind]]+np.arange(1, len(sample_true)+1,1), sample_true[::-1], label='True RUL')
ax[0].plot(x_ds[md_ind[ind]]+np.arange(1, len(sample_true)+1,1), sample_pred[::-1], 'r', label='Predicted RUL')
ax[0].set_xlim(xmin=-5)
ax[0].set_ylabel('RUL')
ax[0].legend()
ax[1].plot(range(len(no_deg_sample)), no_deg_sample, color='g')
ax[1].plot(range(len(no_deg_sample), len(no_deg_sample)+len(deg_sample)), deg_sample, color='r')
ax[1].axhline(threshold, c='k', label='MD threshold')
ax[1].set_xlim(xmin=-5)
ax[1].set_xlabel('Operational cycles')
ax[1].set_ylabel('MD')
ax[1].legend()
fig.savefig(os.path.join('plots','FD004_samples_{}.png'.format(md_ind[ind]+1)),
bbox_inches='tight', pad_inches=0)
def RUL_RMSE_stats_non_degraded_samples():
dataset_name = 'CMAPSS_unfiltered'
results_dir = os.path.join(os.getcwd(), 'training_results', dataset_name, model)
for sub_dataset in ['FD001','FD002','FD003','FD004']:
rmse = []
runs = [i for i in os.listdir(os.path.join(results_dir, sub_dataset)) if 'ignored' in i]
for run in runs:
data = np.load(os.path.join(results_dir, sub_dataset, run), allow_pickle=True)
y_true = np.asarray([int(i[0]) for i in data['y_true']])
y_pred = np.asarray([int(i[0]) for i in data['y_pred']])
rmse.append(rmse_eval(y_true, y_pred))
print(sub_dataset+' mean RMSE: {:.2f}'.format(np.mean(rmse)))
print(sub_dataset+' std. RMSE: {:.2f}'.format(np.std(rmse)))
def end_RUL_prediction():
for sub_dataset in ['FD001','FD002','FD003','FD004']:
res_rmse = pd.read_csv(os.path.join(results_dir, sub_dataset, sub_dataset+'.csv'))
best_model_name = 'model_'+str(res_rmse.iloc[res_rmse[' RMSE'].idxmin(),0])+'_results.npz'
res_data = np.load(os.path.join(results_dir, sub_dataset, best_model_name),allow_pickle=True)
y_true = np.asarray([i[0] for i in res_data['y_true']]).flatten()
y_pred = np.asarray([i[0] for i in res_data['y_pred']]).flatten()
sample_rmse = rmse_eval(y_true, y_pred)
sorted_idx = np.argsort(y_true)
y_true = y_true[sorted_idx]
y_pred = y_pred[sorted_idx]
plt.figure()
plt.plot(np.arange(1, len(y_true)+1,1), y_true[::-1], label='True RUL')
plt.plot(np.arange(1, len(y_true)+1,1), y_pred[::-1], 'r', label='Predicted RUL')
plt.xlabel('Engine (sorted by RUL)')
plt.ylabel('Remaining Useful Life')
plt.legend()
plt.text(0.75, 0.7, 'RMSE '+'{:.2f}'.format(sample_rmse),
fontsize=10, bbox=dict(facecolor='green', alpha=0.3), transform=plt.gca().transAxes)
plt.savefig(os.path.join('plots','RUL_results',sub_dataset+'_end_RUL.png'),
bbox_inches='tight', pad_inches=0)
x_train = scaler_x.fit_transform(x_train)
x_valid = scaler_x.transform(x_valid)
x_test = scaler_x.transform(x_test)
saver = ResultsSaver(results_dir, sub_dataset, sub_dataset)
Model = ANNModel(x_train, y_train, x_valid, y_valid, model_type=model_name)
for i in range(10):
model = Model.model_train(params)
y_pred = model.predict(x_test)
prediction_plots(y_test,
y_pred,
plot_name=sub_dataset + '_iter_' + str(i + 1),
save_dir=os.path.join(results_dir, sub_dataset))
test_loss = rmse_eval(y_test, y_pred, sub_dataset)
saver.save_iter(i + 1, test_loss)
# Test set results saving
x_test_samples, y_test_samples = dataset_loader.get_samples(
sub_dataset, 'test')
y_pred = []
for x_sample in x_test_samples:
y_pred.append(model.predict(scaler_x.transform(x_sample)))
np.savez(os.path.join(results_dir, sub_dataset,
'model_' + str(i + 1) + '_results.npz'),
y_true=y_test_samples,
y_pred=y_pred)
if dataset_name == 'CMAPSS_unfiltered':
x_test_samples, y_test_samples = dataset_loader.get_samples(
x_train = scaler_x.fit_transform(x_train)
x_valid = scaler_x.transform(x_valid)
x_test = scaler_x.transform(x_test)
saver = ResultsSaver(results_dir, sample_name, sample_name)
Model = ANNModel(x_train,
y_train,
x_valid,
y_valid,
x_test=x_test,
y_test=y_test,
model_type=model_name)
for i in range(10):
model = Model.model_train(params)
y_pred = model.predict(x_test)
prediction_plots(y_test,
y_pred,
plot_name=sample_name + '_iter_' + str(i + 1),
save_dir=os.path.join(results_dir, sample_name))
test_loss = rmse_eval(y_test, y_pred, sample_name)
saver.save_iter(i + 1, test_loss)
# Test set results saving
np.savez(os.path.join(results_dir, sample_name,
'model_' + str(i + 1) + '_results.npz'),
y_true=y_test,
y_pred=y_pred)
Model.model_plot(model, results_dir, model_name=dataset_name + '_model.png')