Failure prediction is the core output of a predictive maintenance system.

It quantifies the future risk of a breakdown.

This is not a simple guess; it's a calculated forecast based on the analysis of a wide range of data.

The data used typically includes: 1.

**Telematics and Sensor Data:** This is the most critical input.

It includes real-time information from the vehicle's CAN bus, such as engine RPM, coolant temperature, oil pressure, throttle position, and fault codes (DTCs).

Additional sensors can provide data on vibration, acoustics, and temperature.

2.

**Operational Data:** This includes information about how the vehicle is used, such as mileage, operating hours, load weights, and types of routes (e.

g.

, city vs.

highway).

3.

**Maintenance History:** Records of past repairs, component replacements, and servicing provide a valuable baseline for a specific vehicle's health.

4.

**Environmental Data:** Factors like ambient temperature and humidity can also influence component lifespan and are sometimes included in the models.

Machine learning algorithms process this combined data to identify correlations and patterns that precede failures.

The output is typically a 'Remaining Useful Life' (RUL) estimate or a probability score of failure within a future time period, allowing managers to intervene before the breakdown occurs.