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Trimble Rail Solutions|10 August 2021

R2M System

Real-Time Remote Diagnostic Monitoring

Trimble® R2M system provides a comprehensive view of fleet status including specific faults and identifies potential faults that may arise.

Real-time intelligent decision-making

Bring real-time fault information into the control room for intelligent decisions and planning with the Trimble R2M remote diagnostic system. It analyses and detects anomalies in on-vehicle component behaviour to identify component health and possible impacts. R2M processes diagnostic data received from each rail vehicle in real-time. This provides a comprehensive view of the overall fleet status including any specific vehicle faults and identifies any potential faults that may arise. Trimble R2M’s powerful rules engine with customisable rules provides fully configurable technical and operational warnings and alerts which drives reliability growth and mitigates failures.

Condition-based maintenance

Trimble R2M also analyses and detects anomalies, in specific on-vehicle component behaviour, and uses this information to identify the health and status of both the individual component and the possible impact that this behaviour may have on the vehicle and on the overall fleet. It pinpoints areas of difficulty for the vehicle operator enhancing infrastructure management. Moving from time to condition-based maintenance routines increases mean time between faults reducing maintenance and running costs.

Preventing issues improves safety

R2M immediately identifies faults and prevents potential faults improving fleet safety. R2M can also be used after an incident or fault has occurred to replay the history of events and the condition and behaviour of the vehicle that led to the fault or incident.

Trimble® E2M is a rolling stock asset maintenance management system for asset owners that is designed to manage key rail asset data, maintenance programmes, and...
Mining the Data You Need: Trimble Machine Vision Systems Monitor Rolling Stock Even in the Harshest Environments.
Condition data of rail assets already exists but often is not fully utilised or not utilised at all.
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