Carithm Research Paper 01
Most fleet failures are not unpredictable. They happen because maintenance systems assume conditions that do not match real-world operation.
A vehicle is serviced on schedule, passes inspection, and returns to operation with no visible issues.
Days or weeks later, it develops vibration, fluid degradation symptoms, or unexpected mechanical failure.
In most cases, the vehicle was never “healthy” in the operational sense — it was simply not visibly failing yet.
This gap between visible condition and actual mechanical state is where most fleet risk is concentrated.
Standard maintenance systems rely on three assumptions:
In real fleet environments — especially high-heat, dusty, or high-load regions — these assumptions break down.
Heat accelerates fluid breakdown. Dust increases filtration load. Stop-start cycles accelerate mechanical fatigue.
Wear is not uniform — it varies based on how and where the asset operates.
The key blind spot is degradation variance between identical assets.
Two vehicles with the same mileage can be in completely different mechanical states depending on:
This variance is not captured by OEM intervals or visual inspection.
The issue is not lack of maintenance — it is lack of visibility into true component condition.
Most fleets operate using lagging indicators: mileage, calendar intervals, and visual inspection.
These methods detect failure late in the lifecycle, not at the point where intervention is most effective.
Closing this gap requires moving from schedule-based maintenance to lifecycle-aware interpretation of existing vehicle data.
This is the problem Carithm is designed to address — converting service history and operational context into component-level lifecycle visibility, so degradation is understood before it becomes failure.