Semi-Trailer Wheel Vibration Analysis for Early Anomaly Detection

The vibration sensor system is designed to detect multiple types of anomalies including bearing wear, braking system defects, loose wheels, and road conditions using a single integrated platform. The electronic processor analyzes vibration patterns to identify various failure modes, making the system multi-functional and reducing the need for separate detection systems for each anomaly type.

The patent introduces a machine learning model as an intermediary between the vibration sensor and the anomaly identification process. The model processes raw vibration data and translates it into actionable insights about component health and road conditions, bridging the gap between simple vibration measurement and complex diagnostic interpretation.

The system performs preliminary classification of vibration levels into distinct categories (normal, elevated, high) before detailed anomaly analysis. This staged approach allows the machine learning model to process data more efficiently by first filtering out normal conditions and then applying more complex analysis only when necessary, reducing overall computational complexity.

The patent replaces traditional mechanical vibration analysis methods with machine learning and deep learning algorithms. Instead of using complex physical models and thresholds, the system uses trained neural networks to automatically recognize patterns and classify vibration levels, achieving higher precision while managing computational complexity through software-based solutions.