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What does it do?

Rail Power captures vibration signatures and uses AI models to detect change, forecast degradation and surface the likely root cause — supporting predictive maintenance and resilience operations.

  • Low‑power rail/structure‑mounted nodes for long‑term monitoring
  • Change detection + forecasting tuned for infrastructure signals
  • Mechanism‑level insight to make interventions more targeted
  • Dashboards and APIs to integrate into operator workflows

Why it matters

Ageing assets and limited trackside access drive the need for remote monitoring. Predictive maintenance helps reduce emergency call‑outs and disruption minutes.

Indicative outcomes
10–30%
Lower maintenance cost (indicative)
30–50%
Less downtime (indicative)
40–70%
Fewer emergency repairs (indicative)

Use cases

Designed to support both planned works and disruption response.

  • Condition monitoring for rail defects and infrastructure degradation
  • Predictive maintenance planning to avoid failures and line closures
  • Corridor‑level resilience services to reduce passenger disruption
  • Scenario libraries and synthetic data for rare failures (optional)

Impact measurement

KPIs should be defined per corridor using baselines (fault logs, delay attribution, maintenance records). Indicative targets can include:

  • 10–20% fewer unscheduled maintenance events on relevant assets
  • 15–20% fewer disruption minutes linked to infrastructure issues
  • Improved detection‑to‑response time for emerging faults

Solution

Predictive maintenance

Forecast degradation progression to move from time‑based to condition‑based interventions.

Failure mechanism insight

Explainable analytics to distinguish likely fault types and reduce investigative time.

Resilience services

Corridor‑level monitoring to support disruption response and multimodal passenger service continuity.