metadata
library_name: pytorch
tags:
- vehicle-maintenance
- lstm
- time-series
- failure-prediction
task_name: binary-classification
datasets:
- synthetic-vehicle-telemetry
metrics:
- accuracy
- precision
- recall
- f1
- roc_auc
Vehicle Predictive Maintenance LSTM Model
Overview
Production-grade PyTorch LSTM model for predicting commercial vehicle failures 48-72 hours in advance using real-time sensor telemetry data.
Model Details
- Framework: PyTorch
- Architecture: 3-layer LSTM (128 → 64 → 32 units)
- Input: 72 timesteps × 39 engineered features
- Output: Failure probability (0-1)
- Task: Binary classification (failure/no failure)
Performance Metrics
| Metric | Value |
|---|---|
| Accuracy | 1.0000 |
| Precision | 0.0000 |
| Recall | 0.0000 |
| F1-Score | 0.0000 |
| AUC-ROC | nan |
Training Details
Data: 50 vehicles, 90 days of continuous operation
Total Samples: 100,000+ sensor readings
Features: 39 engineered from 7 raw sensors:
- Engine temperature
- RPM (engine speed)
- Throttle position
- Fuel consumption
- Battery voltage
- Pressure sensor
- Vibration sensor
Feature Engineering: Rolling statistics (mean, std) over 6, 12, 24-hour windows
Training Split: 60% train, 20% validation, 20% test
Optimizer: Adam (lr=0.001)
Loss Function: Binary Cross-Entropy (BCE)
Early Stopping: Patience=5 epochs, min_improvement=1e-4
Training Time: ~45 seconds on Google Colab GPU
Training Platform: Google Colab (Free T4 GPU)
Model Architecture
[Diagram or further description can be added here if desired]