def main(rms_data):
global model
RUL_preds = []
total_data = rms_data[0].flatten() # (858, 1)
fig = plt.figure(figsize=(19, 9))
"Moving Average"
MA_data= np.convolve(total_data, np.ones((mv_size,)) / mv_size, mode='valid')[:610] # (818, 1)
RUL_true = [(num, rms) for num, rms in enumerate(MA_data) if thld_failure <= rms][0][0] # (818, 1)
if type_ == 'linear':
model = LinearRegression()
elif type_ == 'exponential':
model = Nonlinear_Regression()
elif type_ == 'log':
model = Nonlinear_Regression()
elif type_ == 'Lloyd-Lipow':
model = Nonlinear_Regression()
elif type_ == 'gompertz':
model = Nonlinear_Regression()
elif type_ == 'power':
model = Nonlinear_Regression()
else:
assert print('Unsupported regression model', file=sys.stderr)
for cnt, tc in sorted(enumerate(tc_range)):
ax1 = fig.add_subplot(4, 5, (1, 18))
ax2 = fig.add_subplot(4, 5, (14, 20))
observed_data = MA_data[:tc]
time_curr_range = current_time(tc, n_window)
time_pred_range = prediction_time(tc, n_window, range_pred)
ax1.set_title('RUL prediction - {} regression | time range: {}m ~ {}m'.format(type_, tc - n_window, tc))
ax1.plot(MA_data, '.', label='total data')
ax1.plot(observed_data, '.', label='observed_data')
ax1.text(0, thld_safe + 0.05, str(thld_safe), color='c', fontsize=13, weight='bold')
ax1.text(0, thld_fault + 0.05, str(thld_fault), color='r', fontsize=13, weight='bold')
ax1.text(0, thld_failure + 0.05, str(thld_failure), color='r', fontsize=13, weight='bold')
ax1.axvline(x=tc - n_window)
ax1.axvline(x=tc)
ax1.axhline(y=thld_safe, color='c', linestyle='-', label='Threshold safe')
ax1.axhline(y=thld_fault, color='r', linestyle='--', label='Threshold fault')
ax1.axhline(y=thld_failure, color='r', linestyle='-', label='Threshold failure')
ax1.axvspan(tc - n_window, tc, facecolor='gray', alpha=0.5)
ax1.set_xlabel('Times (m)')
ax1.set_ylabel('RMS')
ax1.set_xlim([-10, 900])
ax1.set_ylim([min(observed_data) - 0.2, 10])
ax1.annotate('True RUL: {}'.format(RUL_true), xy=(tc, 5), fontsize=12, weight='bold')
if np.prod(observed_data[time_curr_range] >= thld_fault):
if type_ == 'linear':
model.fit(time_curr_range[:,np.newaxis], observed_data[time_curr_range])
y_pred = model.predict(time_pred_range[:,np.newaxis])
try:
RUL_pred = np.where(y_pred > thld_failure)[0][0] + (tc - n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred), file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred), file=sys.stderr)
ax1.plot(np.arange(tc-n_window,tc+range_pred), y_pred, 'g-', linewidth=3)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred), xy=(tc, 4.8), fontsize=12, color='red', weight='bold')
elif type_ == 'exponential':
popt, _ = model.fit_exp(time_curr_range, observed_data[time_curr_range])
y_pred = model.exponential_func(prediction_time(tc, n_window, range_pred), *popt)
try:
RUL_pred = np.where(y_pred > thld_failure)[0][0] + (tc - n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred), file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred), file=sys.stderr)
ax1.plot(np.arange(tc-n_window,tc+range_pred)[:len(y_pred)], y_pred, 'g-', linewidth=3)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred), xy=(tc, 4.8), fontsize=12, color='red', weight='bold')
elif type_ == 'log':
popt, _ = model.fit_log(time_curr_range, observed_data[time_curr_range])
y_pred = model.log_func(prediction_time(tc, n_window, range_pred), *popt)
try:
RUL_pred = np.where(y_pred > thld_failure)[0][0] + (tc - n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred), file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred), file=sys.stderr)
ax1.plot(np.arange(tc-n_window, tc+range_pred), y_pred, 'g-', linewidth=3)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred), xy=(tc, 4.8), fontsize=12, color='red', weight='bold')
elif type_ == 'Lloyd-Lipow':
popt, _ = model.fit_lipow(time_curr_range, observed_data[time_curr_range])
y_pred = model.lipow_func(prediction_time(tc, n_window, range_pred), *popt)
try:
RUL_pred = np.where(y_pred > thld_failure)[0][0] + (tc - n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred), file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred), file=sys.stderr)
ax1.plot(np.arange(tc - n_window, tc + range_pred), y_pred, 'g-', linewidth=3)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred), xy=(tc, 4.8), fontsize=12, color='red',
weight='bold')
elif type_ == 'gompertz':
popt, _ = model.fit_gom(time_curr_range, observed_data[time_curr_range])
y_pred = model.gom_func(prediction_time(tc, n_window, range_pred), *popt)
try:
RUL_pred = np.where(y_pred > thld_failure)[0][0] + (tc - n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred), file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred), file=sys.stderr)
ax1.plot(np.arange(tc - n_window, tc + range_pred), y_pred, 'g-', linewidth=3)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred), xy=(tc, 4.8), fontsize=12, color='red',
weight='bold')
elif type_ == 'power':
popt, _ = model.fit_pow(time_curr_range, observed_data[time_curr_range])
y_pred = model.pow_func(prediction_time(tc, n_window, range_pred), *popt)
try:
RUL_pred = np.where(y_pred > thld_failure)[0][0] + (tc - n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred), file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred), file=sys.stderr)
ax1.plot(np.arange(tc - n_window, tc + range_pred), y_pred, 'g-', linewidth=3)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred), xy=(tc, 4.8), fontsize=12, color='red',
weight='bold')
else:
RUL_preds += [590 + tc_range[cnt]]
ax2.set_title('RUL curve')
ax2.set_xlabel('Times (m)')
ax2.set_ylabel('RUL')
ax2.set_xlim([-10, RUL_true+20])
ax2.set_ylim([-10, RUL_true+20])
ax2.plot([0, RUL_true], [RUL_true, 0], label='True RUL curve')
ax2.plot(tc_range[:len(RUL_preds)], np.array(RUL_preds) - tc_range[:len(RUL_preds)], label='predicted RUL curve')
ax1.legend(loc='upper left', bbox_to_anchor=(0.01, 0.6))
ax2.legend(loc='upper left', bbox_to_anchor=(0.01, 0.6))
plt.tight_layout()
# "Screen capture at specific time "
# if tc == 605:
# plt.savefig('D:\\Onepredict_MK\\01 Solution 3 Team documents\\02 RUL Modeler\\관련 자료\\Regression capture image\\{} regression_{}time'.format(type_, tc))
# print('Capture done!')
plt.pause(speed_search)
plt.clf()
os.system('pause')
def main(rms_data):
global model
RUL_preds = []
total_data = rms_data[0].flatten() # (858, 1)
fig = plt.figure(figsize=(19, 9))
# ax1 = fig.add_subplot(2, 2, (1, 3))
# ax2 = fig.add_subplot(2, 2, 4)
"Moving Average"
MA_data = np.convolve(total_data,
np.ones((args.mv_size, )) / args.mv_size,
mode='valid')[:610] # (818, 1)
RUL_true = [(num, rms) for num, rms in enumerate(MA_data)
if 9.0 <= rms <= 9.1][0][0] # (818, 1)
if args.type == 'linear':
model = LinearRegression()
elif args.type == 'exponential':
model = Nonlinear_Regression()
elif args.type == 'log':
model = Nonlinear_Regression()
elif args.type == 'lipow':
model = Nonlinear_Regression()
elif args.type == 'gom':
model = Nonlinear_Regression()
elif args.type == 'pow':
model = Nonlinear_Regression()
elif args.type == 'GP':
kernel_RBF = kernels.ConstantKernel(1.0) * kernels.RBF(
length_scale=1.0)
model = GaussianProcessRegressor(kernel=kernel_RBF, alpha=0.4**2)
else:
assert print('Unsupported regression model', file=sys.stderr)
for cnt, tc in sorted(enumerate(args.tc)):
ax1 = fig.add_subplot(2, 2, (1, 3))
ax2 = fig.add_subplot(2, 2, 4)
observed_data = MA_data[:tc]
time_curr_range = current_time(tc, args.n_window)
time_pred_range = prediction_time(tc, args.n_window, args.range_pred)
if np.prod(observed_data[time_curr_range] >= args.thld_fault):
if args.type == 'linear':
model.fit(time_curr_range[:, np.newaxis],
observed_data[time_curr_range])
y_pred = model.predict(time_pred_range[:, np.newaxis])
try:
RUL_pred = np.where(y_pred > args.thld_failure)[0][0] + (
tc - args.n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred),
file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred),
file=sys.stderr)
ax1.plot(np.arange(tc - args.n_window, tc + args.range_pred),
y_pred, '.')
ax1.annotate('Predicted RUL: {}'.format(RUL_pred),
xy=(tc, 4.8),
fontsize=12,
color='red',
weight='bold')
elif args.type == 'exponential':
popt, _ = model.fit_exp(time_curr_range,
observed_data[time_curr_range])
y_pred = model.exponential_func(
prediction_time(tc, args.n_window, args.range_pred), *popt)
try:
RUL_pred = np.where(y_pred > args.thld_failure)[0][0] + (
tc - args.n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred),
file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred),
file=sys.stderr)
elif args.type == 'log':
popt, _ = model.fit_log(time_curr_range,
observed_data[time_curr_range])
y_pred = model.log_func(
prediction_time(tc, args.n_window, args.range_pred), *popt)
try:
RUL_pred = np.where(y_pred > args.thld_failure)[0][0] + (
tc - args.n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred),
file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred),
file=sys.stderr)
ax1.plot(np.arange(tc - args.n_window, tc + args.range_pred),
y_pred)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred),
xy=(tc, 4.8),
fontsize=12,
color='red',
weight='bold')
elif args.type == 'lipow':
popt, _ = model.fit_lipow(time_curr_range,
observed_data[time_curr_range])
y_pred = model.lipow_func(
prediction_time(tc, args.n_window, args.range_pred), *popt)
try:
RUL_pred = np.where(y_pred > args.thld_failure)[0][0] + (
tc - args.n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred),
file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred),
file=sys.stderr)
ax1.plot(np.arange(tc - args.n_window, tc + args.range_pred),
y_pred)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred),
xy=(tc, 4.8),
fontsize=12,
color='red',
weight='bold')
elif args.type == 'gom':
popt, _ = model.fit_gom(time_curr_range,
observed_data[time_curr_range])
y_pred = model.gom_func(
prediction_time(tc, args.n_window, args.range_pred), *popt)
try:
RUL_pred = np.where(y_pred > args.thld_failure)[0][0] + (
tc - args.n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred),
file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred),
file=sys.stderr)
ax1.plot(np.arange(tc - args.n_window, tc + args.range_pred),
y_pred)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred),
xy=(tc, 4.8),
fontsize=12,
color='red',
weight='bold')
elif args.type == 'pow':
popt, _ = model.fit_pow(time_curr_range,
observed_data[time_curr_range])
y_pred = model.pow_func(
prediction_time(tc, args.n_window, args.range_pred), *popt)
try:
RUL_pred = np.where(y_pred > args.thld_failure)[0][0] + (
tc - args.n_window)
RUL_preds += [RUL_pred]
print('Predicted RUL: {}m'.format(RUL_pred),
file=sys.stderr)
except:
RUL_pred = 'Unestimatable'
RUL_preds += [RUL_preds[-1]]
print('Predicted RUL: {}'.format(RUL_pred),
file=sys.stderr)
ax1.plot(np.arange(tc - args.n_window, tc + args.range_pred),
y_pred)
ax1.annotate('Predicted RUL: {}'.format(RUL_pred),
xy=(tc, 4.8),
fontsize=12,
color='red',
weight='bold')
elif args.type == 'GP':
"MLE를 이용하여 데이터 fitting"
model.fit(time_curr_range[:, np.newaxis],
observed_data[time_curr_range])
"Compute posterior predictive mean and variance"
mu_s, cov_s = model.predict(time_pred_range[:, np.newaxis],
return_cov=True)
samples = np.random.multivariate_normal(mu_s.ravel(), cov_s, 3)
plot_gp(mu_s, cov_s, time_pred_range,
time_curr_range[:, np.newaxis], observed_data, samples)
else:
RUL_preds += [590 + args.tc[cnt]]
ax1.set_title(
'RUL prediction - linear regression | time range: {}m ~ {}m'.
format(tc - args.n_window, tc))
ax1.plot(MA_data, '.', label='total data')
ax1.plot(observed_data, '.', label='observed_data')
ax1.text(0,
args.thld_safe + 0.05,
str(args.thld_safe),
color='c',
fontsize=13,
weight='bold')
ax1.text(0,
args.thld_fault + 0.05,
str(args.thld_fault),
color='r',
fontsize=13,
weight='bold')
ax1.text(0,
args.thld_failure + 0.05,
str(args.thld_failure),
color='r',
fontsize=13,
weight='bold')
ax1.axvline(x=tc - args.n_window)
ax1.axvline(x=tc)
ax1.axhline(y=args.thld_safe,
color='c',
linestyle='-',
label='Threshold safe')
ax1.axhline(y=args.thld_fault,
color='r',
linestyle='--',
label='Threshold fault')
ax1.axhline(y=args.thld_failure,
color='r',
linestyle='-',
label='Threshold failure')
ax1.axvspan(tc - args.n_window, tc, facecolor='gray', alpha=0.5)
ax1.set_xlabel('Times (m)')
ax1.set_ylabel('RMS')
ax1.set_xlim([-10, 900])
ax1.set_ylim([min(observed_data) - 0.2, 10])
ax1.annotate('True RUL: {}'.format(RUL_true),
xy=(tc, 5),
fontsize=12,
weight='bold')
ax2.set_title('RUL curve')
ax2.set_xlabel('Times (m)')
ax2.set_ylabel('RUL')
ax2.set_xlim([-10, RUL_true + 20])
ax2.set_ylim([-10, RUL_true + 20])
ax2.plot([0, RUL_true], [RUL_true, 0], label='True RUL curve')
ax2.plot(args.tc[:len(RUL_preds)],
np.array(RUL_preds) - args.tc[:len(RUL_preds)],
label='predicted RUL curve')
ax1.legend(loc='upper left', bbox_to_anchor=(0.01, 0.6))
ax2.legend(loc='upper left', bbox_to_anchor=(0.01, 0.6))
plt.tight_layout()
plt.pause(0.1)
plt.clf()
os.system('pause')
