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99ee88a 0da1da8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | from datetime import datetime
import numpy as np
import pandas as pd
def generate_cyclical_features(timestamp):
if isinstance(timestamp, str):
timestamp = datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
hour = timestamp.hour
dayofweek = timestamp.weekday()
dayofyear = timestamp.timetuple().tm_yday
month = timestamp.month
features = {
"hour_sin": np.sin(2 * np.pi * hour / 24),
"hour_cos": np.cos(2 * np.pi * hour / 24),
"dayofweek_sin": np.sin(2 * np.pi * dayofweek / 7),
"dayofweek_cos": np.cos(2 * np.pi * dayofweek / 7),
"dayofyear_sin": np.sin(2 * np.pi * dayofyear / 365),
"dayofyear_cos": np.cos(2 * np.pi * dayofyear / 365),
"month_sin": np.sin(2 * np.pi * month / 12),
"month_cos": np.cos(2 * np.pi * month / 12),
}
return features
def convert_cyclical_to_original(
hour_sin,
hour_cos,
dayofweek_sin,
dayofweek_cos,
dayofyear_sin,
dayofyear_cos,
month_sin,
month_cos,
):
hour = np.arctan2(hour_sin, hour_cos) * 24 / (2 * np.pi)
hour = int(np.round(hour % 24))
dayofweek = np.arctan2(dayofweek_sin, dayofweek_cos) * 7 / (2 * np.pi)
dayofweek = int(np.round(dayofweek % 7))
dayofyear = np.arctan2(dayofyear_sin, dayofyear_cos) * 365 / (2 * np.pi)
dayofyear = int(np.round(dayofyear % 365))
dayofyear = max(1, dayofyear)
month = np.arctan2(month_sin, month_cos) * 12 / (2 * np.pi)
month = int(np.round(month % 12))
month = 12 if month == 0 else month
return {
"hour": hour,
"dayofweek": dayofweek,
"dayofyear": dayofyear,
"month": month,
}
def create_sequences(data, window_size=32):
sequences = []
for i in range(len(data) - window_size + 1):
sequences.append(data[i : i + window_size])
return np.array(sequences)
def prepare_prediction_data(sensor_data, timestamp, scaler_x, window_size=32):
try:
sensor_features = {
"Air temperature [K]": sensor_data.get("air_temp", 0),
"Process temperature [K]": sensor_data.get("process_temp", 0),
"Rotational speed [rpm]": sensor_data.get("rotational_speed", 0),
"Torque [Nm]": sensor_data.get("torque", 0),
"Tool wear [min]": sensor_data.get("tool_wear", 0),
}
cyclical_features = generate_cyclical_features(timestamp)
all_features = {**sensor_features, **cyclical_features}
column_order = [
"Air temperature [K]",
"Process temperature [K]",
"Rotational speed [rpm]",
"Torque [Nm]",
"Tool wear [min]",
"hour_sin",
"hour_cos",
"dayofweek_sin",
"dayofweek_cos",
"dayofyear_sin",
"dayofyear_cos",
"month_sin",
"month_cos",
]
X_new = pd.DataFrame([all_features])
X_new = X_new[column_order]
if scaler_x is None:
raise ValueError("scaler_X not loaded.")
X_scaled = scaler_x.transform(X_new)
X_sequence = np.repeat(X_scaled, window_size, axis=0).reshape(
1, window_size, -1
)
return X_sequence
except Exception as e:
print(f"Error preparing prediction data: {str(e)}")
import traceback
traceback.print_exc()
return None
def create_timestamp_from_predictions(predictions, sensor_timestamp=None):
try:
hour = predictions.get("hour", 0)
dayofyear = predictions.get("dayofyear", 1)
if sensor_timestamp:
if isinstance(sensor_timestamp, str):
input_dt = datetime.fromisoformat(
sensor_timestamp.replace("Z", "+00:00")
)
else:
input_dt = sensor_timestamp
year = input_dt.year
else:
year = datetime.now().year
predicted_dt = datetime.strptime(f"{year}-{dayofyear}", "%Y-%j")
predicted_dt = predicted_dt.replace(hour=hour, minute=0, second=0)
return predicted_dt.isoformat()
except Exception as e:
print(f"Error creating timestamp: {str(e)}")
return None
def prepare_sensor_data_for_anomaly(sensor_data, preprocessor):
try:
sensor_features = {
"Air temperature [K]": sensor_data.get("air_temp"),
"Process temperature [K]": sensor_data.get("process_temp"),
"Rotational speed [rpm]": sensor_data.get("rotational_speed"),
"Torque [Nm]": sensor_data.get("torque"),
"Tool wear [min]": sensor_data.get("tool_wear"),
}
if None in sensor_features.values():
print("Error: Missing sensor data!")
return None
X = pd.DataFrame([sensor_features])
if preprocessor is None:
raise ValueError("preprocessor not loaded.")
X_transormed = preprocessor.transform(X)
return X_transormed
except Exception as e:
print(f"Error preparing prediction data: {str(e)}")
import traceback
traceback.print_exc()
return None
def calculate_risk_score(confidence, severity):
severity_weight = severity / 5.0
risk_score = confidence * severity_weight * 100
return risk_score
def get_risk_level(risk_score):
if risk_score >= 80:
return "Critical"
elif risk_score >= 60:
return "High"
elif risk_score >= 40:
return "Medium"
elif risk_score >= 20:
return "Low"
else:
return "Very Low"
def get_failure_severity(prediction_index):
severity_mapping = {
0: 4, # Heat Dissipation Failure - High
1: 4, # Overstrain Failure - High
2: 5, # Power Failure - Critical
3: 2, # Random Failures - Low-Medium
4: 3, # Tool Wear Failure - Medium
}
return severity_mapping.get(prediction_index, 3)
def get_failure_type_name(prediction_index):
failure_types = {
0: "Heat Dissipation Failure",
1: "Overstrain Failure",
2: "Power Failure",
3: "Random Failures",
4: "Tool Wear Failure",
}
return failure_types.get(prediction_index, "Unknown Failure")
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