Predictive Maintenance

With the rapid advancement of network technologies and sensors, monitoring sensor data such as pressure, temperature, current, vibration and other electrical variables has become much more important. With advances in big data and artificial intelligence, solutions can be developed to prevent breakdowns and predict the remaining life of equipments.

Our commercial vehicle management system platform incorporates an advanced predictive maintenance system that leverages real-time telemetry and historical performance data to enhance overall fleet efficiency. By continuously analyzing parameters collected directly from the vehicle’s CAN network—the system not only reports current operating conditions but also forecasts potential faults and maintenance requirements.

Unlike traditional preventive maintenance, which relies on fixed schedules, our predictive maintenance approach applies machine learning models to detect early anomalies and estimate component wear in advance. This enables fleet managers to plan interventions precisely when needed, reducing unplanned downtime, optimizing maintenance resources, and extending vehicle lifespan.

Predictive Analysis Methodology

The system predicts potential future malfunctions by aggregating and analyzing historical operational data collected from connected devices. Using advanced data analytics and pattern recognition algorithms, it identifies recurring fault behaviors and correlations among key parameters.