OmniRail

Collect. Merge. Analyze. Anticipate.

OmniRail turns the vast volumes of raw data generated by track inspection and on-board monitoring systems into actionable, predictive insight. Infrastructure managers and engineering teams use it to move from reactive, calendar-based maintenance to true Condition-Based Maintenance — optimizing cycles, cutting operating costs and maximizing network safety for both freight and passengers.

Multi-source data ingestion

OmniRail unifies on-board accelerometers, traditional track geometry, trolley measurements, IoT sensors and external ERPs into a single, harmonized data layer — whatever combination of assets you already run.

Built for Big Data, ready on day one

A modern stack — React, Spring Boot and Elasticsearch — indexes terabytes of telemetry and serves complex, time-based queries instantly. Deploy on-premise or in the cloud; no specialist tooling required to start working with your data.

One platform for the whole network

OmniRail consolidates track, signaling, rolling stock and energy telemetry into one workspace — so a single team can monitor every asset, prioritize interventions and coordinate internal shifts with external contractors.

Higher safety, lower operating costs

Continuous, threshold-based anomaly detection catches incipient geometry faults and rail defects before they escalate — cutting unplanned repairs, emergency labor and material waste while raising the safety bar across the network.

Why OmniRail?

Periodic manual checks tell you the condition of the track on the day the inspection happened. OmniRail tells you the condition right now — and where it is heading. By contextualizing accelerations, geometry, trolley data and track events on the same kilometer-point axis, maintainers can qualitatively and quantitatively assess every section of track and act before defects become incidents.

From the same workspace you can define operating limits, set warning and safety thresholds, plan interventions and trigger work orders — closing the loop between detection and action.

Onboard Track Monitoring System

OmniRail allows to detect irregularities on railtrack and quickly react to failures. Using a kinematic approach, the computer vision system and inertial sensors map the entire network through which the equipped trains pass. The best alternative solution to traditional auscultation. No more heavy trolleys and expensive laboratory vehicles. Hardware solution powered by Virtualmech.

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SKF bogies monitoring, integrated as a native data source

OmniRail integrates natively with SKF Multilog IMx units installed on the bogies. A single instrumented train can survey the entire network: performance data is relayed to OmniRail, processed against the configured thresholds, and any exceedances are surfaced automatically in the Defect List — alarms reach the maintenance team without anyone having to log into a separate tool.

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Multidimensional Data Charts

Overlay 30-day-ago data on today’s telemetry to spot degradation at a glance. Fine-tune the focus by ±100 m
Defect List — prioritized interventions

OmniRail consolidates every parameter anomaly that lands on the same kilometer point into a single, actionable row. Orange flags pre-alerts, red flags critical defects — so maintainers focus on the highest-priority sections first.
Closing the loop — Maintenance Planner

Work orders generated from a defect inherit the exact line, track and a ±50 m work segment around the anomaly. Coordinate internal shifts and external contractors side by side with a full audit trail back to the data that triggered the order.

Cover the riskiest sections with a complementary IoT sensor

For sidings, problematic joints and stretches of track with significant temperature gradients, OmniRail pairs natively with Blacknest SURFACE — a compact, magnet-mounted IoT sensor that streams rail temperature, tilt and vibration continuously, with LTE-M1 connectivity and no track-side repeaters.
Its data flows straight into OmniRail, where it sits alongside on-board accelerations and traditional geometry on the same kilometer-point axis. AI-driven analysis helps anticipate rail breaks on the sections that need it most, with installation that is both technically and economically feasible.