Djinious
Book a demo
Rail fleet telematicsMobility

Condition monitoring for a vehicle fleet

Turn a device feed into a fleet you can ask questions about, and a maintenance plan you can defend.

DjiniousDataKnowledge base
DjiniousData
The AcmeRail fleet-telematics dashboard in DjiniousData: telemetry-point, speed, distance, fuel, coolant and idle-alert tiles above a live fleet map of northern France and Belgium, an operating-state donut, and per-locomotive breakdowns.

Eight locomotives report position, engine load, coolant temperature and bus voltage; a depot CMMS carries two years of work orders against the same units. Modelled as locomotives, places and fleet events, the two become one picture: which unit is over its overhaul interval, which is overheating, which has not moved in a fortnight — each answer opening onto the readings behind it.

AcmeRail is a fictional freight operator seeded into the development stack. Its fleet, its depots, its telematics vendor and its CMMS are invented; the ingest that loaded them, the ontology that models them and the findings drawn from them are the product’s own.

How it runs

  1. Ingest

    Ingest the device feed and the work-order export; both land as knowledge objects with provenance.

    File ingest

  2. Model

    Model locomotive, place, fleet and fleet event as types, at runtime.

    Runtime ontology

  3. Resolve

    Materialize the units from their own latest telemetry and relate them into a fleet graph.

    Knowledge graph

  4. Act

    Put threshold rules on coolant and bus voltage, per unit, and let the findings open investigations.

    Alert rules

The fleet graph

The places are the ones the telemetry named; the edges are what it supports.

DjiniousData
The DjiniousData knowledge graph explorer showing the AcmeRail fleet node connected to its eight locomotives and to the AcmeRail organization.
The fleet, its eight units and the organization they belong to, built from the positions the units actually reported.

Every figure was measured

The findings in the captures are not written copy. The seed reads the ingested telemetry, computes the fleet’s actual engine hours, peak coolant temperatures and minimum bus voltages, and composes the finding text from those numbers.

Measured summaries

Signal summaries interpolate measured aggregates, never hard-coded figures.

Evidence references

Each signal carries references to the knowledge objects it was drawn from.

Thresholds set against the fleet

The alert thresholds are set against the measured fleet, so the seeded stack ships with real firings rather than four rules that have never fired.

One unit, one thread

One locomotive breaches both the thermal and the electrical rule — the same unit the investigation case and the deep-dive report are about.

From rule to report

DjiniousData
The DjiniousData alert rules page listing four fleet telemetry rules — coolant temperature high, low main battery, locomotive not moving, and device idle alerts raised — with their conditions and channels.
Four rules over one source, each aggregating a field per locomotive.
DjiniousData
The DjiniousData alert history listing eleven open alerts raised by four fleet rules, each naming the locomotive, the observed value and the threshold it crossed.
Every firing keeps the value it observed beside the threshold it crossed, so a rule that is too sensitive is visible as such rather than argued about.
DjiniousData
An open investigation case for locomotive G1206-AR1042 consolidating its cooling and electrical risk signals, with three collected evidence items, its tags, its owner and its activity trail.
The case that consolidates the fleet’s two concurrent health flags on one unit.
DjiniousData
A generated AcmeRail fleet maintenance and health briefing, with an executive summary, per-unit fleet status, health flags and recommended actions, each section citing the telemetry it draws on.
The briefing quotes distance, engine hours, peak coolant and minimum bus voltage per unit — all read from the ingested telemetry rather than written into the prose.

Keep exploring

Related use cases

All use cases
A 4-cell balancing module on the DjiniousLab canvas — four equivalent-circuit cells starting at different states of charge, wired through an active charge-shuttling balancer into per-cell state-of-charge scopesBattery systemsEV battery packAn electric-vehicle battery pack as one multi-domain model — equivalent-circuit cells, a coupled thermal network, a CC-CV charge controller and an active balancer, composed cell → module → pack and run end to end.DjiniousLabA PMSM traction-motor efficiency map — torque against speed coloured by efficiency, with a high-efficiency island peaking near 97% inside the torque-speed envelopeElectric vehiclesFull EV vehicleThe battery pack you sized becomes the car you drive — a complete electric vehicle over the WLTP cycle: drive cycle, traction motor, regen, the battery over the cycle, thermal, and the numbers that sell a car — range, consumption, and 0–100.DjiniousLabDocument intelligenceMaking a document archive answerableA shared drive nobody can query becomes a searchable, cited corpus without a migration project.DjiniousData