Digital Twin for Vehicle Component Life Prediction

The patent creates a digital twin (a virtual copy) of each physical component that replicates its behavior and wear characteristics. This digital replica is trained on historical sensor data from the specific component, allowing the system to predict future wear patterns with high accuracy while accounting for individual component variations. The digital twin continuously learns from actual operating conditions, enabling precise remaining useful life predictions that differ from generic prediction models.

The system dynamically adjusts prediction parameters by training machine learning models on component-specific historical data. Instead of using fixed prediction parameters, the system adapts parameters such as wear rates, degradation patterns, and failure thresholds based on actual sensor measurements from each component. This allows the prediction accuracy to improve over time as more data becomes available, while maintaining reliability through continuous model validation.

The system performs preliminary wear assessment and prediction for each component before servicing is needed. By using the digital twin to forecast remaining useful life with high accuracy, the system can schedule servicing only when and where it is truly needed, rather than performing routine servicing on all components regardless of their actual condition. This preliminary prediction enables proactive planning of maintenance activities.

The servicing schedule is dynamically adjusted based on real-time wear predictions from the digital twin. Instead of fixed periodic servicing, the system continuously updates remaining useful life estimates as new sensor data becomes available, allowing the servicing interval to adapt to actual component degradation patterns. This dynamic approach optimizes servicing timing to match actual component needs.