Autonomous Failure Predictor v2
Overview
Model ini dirancang untuk memprediksi kemungkinan kegagalan sistem humanoid sebelum terjadi kerusakan atau error kritis.
Cocok untuk:
- Predictive maintenance
- Early failure detection
- Autonomous monitoring system
Problem Type
Binary Classification
Input Features
- motor_temperature_celsius
- joint_vibration_level
- battery_voltage
- cpu_load_percent
- sensor_noise_index
- execution_latency_ms
- error_log_frequency
Output
- failure_risk (normal / high_risk)
Model Architecture
- Input Normalization Layer
- Dense(256) + ReLU
- BatchNormalization
- Dense(128) + ReLU
- Dropout(0.4)
- Dense(64) + ReLU
- Dense(1) + Sigmoid
Loss Function
Binary Crossentropy
Optimizer
Adam (learning_rate=0.001)
Metrics
- Accuracy
- Precision
- Recall
- F1 Score
- ROC-AUC
Validation Strategy
- 80/20 Train-Test Split
- Stratified Sampling
- Early Stopping (patience=10)
Expected Performance (Simulated)
- Accuracy: ~94–96%
- F1 Score: ~0.93
- ROC-AUC: ~0.97
Deployment Scenario
- Edge AI module
- Real-time robotic health monitoring
- Safety-critical shutdown trigger system
Version
v2 – Improved regularization and stability tuning