Predictive Maintenance

Predictive maintenance is a major evolution from preventive maintenance. Preventive maintenance is based on fixed intervals (e.g., changing the oil every 15,000 km). It's a 'just-in-case' approach that often leads to replacing parts that are still functional.

What predictive changes

Predictive maintenance, on the other hand, is conditional and personalized. It continuously analyzes real-time operational data: engine vibrations, oil temperature, fault codes, etc. An AI algorithm compares these signals to learned failure patterns. If the model detects an anomaly indicating a high probability of a water pump failure within the next 1000 kilometers, it triggers an alert. The manager can then schedule the intervention in a targeted manner. The benefit is twofold: it prevents a disabling breakdown on the road, and the part is only replaced when it truly reaches the end of its useful life.

The data that feeds the model

Sensors spread across the vehicle read engine temperature, vibration, oil pressure and battery voltage. Those measurements feed an algorithm trained on histories of healthy and failing components: it picks up the quiet anomalies and the sequences that precede a breakdown, where a fixed threshold still sees nothing.

What it saves you paying for

Avoiding an outright breakdown means avoiding cascading damage and a vehicle stranded at the roadside. It also means ordering the part in advance and placing the work in a quiet slot rather than as an emergency. The fleet stays available, the component runs to the end of its useful life, and the safety risk is handled before it turns critical.