Worked example · 25-vehicle mixed fleet
Component-level failure prediction from telematics data
Rooutiq reads the sensor feed your fleet already produces and identifies which component is degrading, how long it has left, and the cost difference between a planned repair and a roadside failure. Below is a full worked example — including the signals behind each call.
This fleet is illustrative, not a customer account. The failure modes, sensor behavior, and repair costs are representative of what the model produces in production.
Open predictions
Soonest failure in 3 days · $37,470 avoidable
Why we flagged this
- —Differential pressure across the DPF rose 41% over 9 days
- —Regeneration cycles now firing every 62 miles, down from 210
- —Exhaust backpressure trending above the 2019 Cascadia baseline
The rest of the fleet
Health scored nightly from telematics
| Vehicle | Type | Status | Health | Last fault | Next service |
|---|---|---|---|---|---|
| Ford E-450 #101 | Box truck | At risk | 52 | P0401 EGR flow | Jul 1 |
| Ford E-450 #102 | Box truck | Monitor | 68 | P0171 lean mixture | Jul 8 |
| Isuzu NPR #103 | Box truck | Healthy | 91 | — | Aug 15 |
| Isuzu NPR #104 | Box truck | At risk | 44 | P0087 fuel pressure | Jun 28 |
| Isuzu NPR #105 | Box truck | Healthy | 88 | — | Sep 1 |
| Freightliner MT45 #106 | Box truck | Monitor | 71 | P2BAD NOx sensor | Jul 12 |
| Freightliner MT45 #107 | Box truck | Healthy | 94 | — | Oct 1 |
| Ford E-450 #108 | Box truck | Monitor | 63 | P0128 coolant temp | Jul 5 |
| Isuzu NPR #109 | Box truck | Healthy | 85 | — | Aug 20 |
| Freightliner MT45 #110 | Box truck | Healthy | 89 | — | Sep 10 |
| Freightliner Cascadia #201 | Semi | At risk | 38 | SPN 3251 DPF pressure | Jun 27 |
| Freightliner Cascadia #202 | Semi | Monitor | 67 | SPN 110 coolant temp | Jul 10 |
| Kenworth T680 #203 | Semi | Healthy | 92 | — | Aug 5 |
| Kenworth T680 #204 | Semi | At risk | 41 | SPN 641 turbo actuator | Jun 29 |
| Freightliner Cascadia #205 | Semi | Healthy | 87 | — | Sep 15 |
| Kenworth T680 #206 | Semi | Monitor | 72 | SPN 3563 intake manifold | Jul 18 |
| Freightliner Cascadia #207 | Semi | Healthy | 83 | — | Aug 22 |
| Kenworth T680 #208 | Semi | Healthy | 90 | — | Sep 30 |
| Ford Transit #301 | Service van | Healthy | 96 | — | Oct 1 |
| Ford Transit #302 | Service van | Monitor | 69 | P0420 catalyst efficiency | Jul 14 |
| Mercedes Sprinter #303 | Service van | Healthy | 97 | — | Nov 1 |
| Mercedes Sprinter #304 | Service van | Healthy | 91 | — | Sep 5 |
| Ford Transit #305 | Service van | Healthy | 84 | — | Aug 18 |
| Mercedes Sprinter #306 | Service van | Healthy | 93 | — | Oct 12 |
| Ford Transit #307 | Service van | Monitor | 61 | P0301 misfire cyl 1 | Jul 9 |
How a prediction is produced
Every prediction above is traceable to specific sensor behavior. Nothing is inferred from vehicle age or mileage schedules alone.
- 01
Signal ingestion
We pull engine, aftertreatment, and drivetrain parameters from your existing telematics feed — typically 30 to 90 second resolution. No additional hardware is installed on the vehicle.
- 02
Deviation from baseline
Each parameter is compared against that vehicle's own operating history and against the same engine family across the fleet, so a reading is judged in context rather than against a fixed threshold.
- 03
Failure matching
Deviation patterns are matched to labeled repair outcomes — verified by the mechanic who did the work — to identify which component is degrading and how quickly.
- 04
Confidence and lead time
Confidence is the model's calibrated hit rate for that failure type, not a marketing number. Predictions below the review threshold are routed to a human instead of being auto-scheduled.
Model accuracy is tracked per failure type and published to every account — including where it is weak. Predictions that turn out wrong are logged as false positives and fed back into the next training cycle.
Run this against your own fleet
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