Earlier fault detection
Identify thermal and vibration changes before they become critical.
Railway bogie monitoring
Catch bogie faults before they stop the train
Bogie monitoring continuously reads temperature and vibration from railway bogies, processes data on-site, and feeds a local AI running inside your own infrastructure.
Edge
processing directly near the data source
No cloud
operational data stays in your infrastructure
Local AI
maintenance insight on your own server
Why it matters
Bogie failures are costly, and largely predictable
Bearing overheating and abnormal vibration are early signs of mechanical wear. Without continuous monitoring, these anomalies often go unnoticed until they cause unplanned stoppages, safety incidents, or costly maintenance disruption.
Early monitoring helps maintenance teams identify abnormal behavior sooner, reduce unexpected downtime and plan interventions more efficiently.
Reduced maintenance disruption
Support planned interventions instead of reactive stoppages.
Secure local processing
Analyze operational data without sending raw signals to the cloud.
Monitoring points
Temperature at bearing areas, vibration on the main frame
Bearing temperature
Temperature is tracked close to axle-box and bearing areas, where overheating can indicate lubrication issues, wear or early-stage faults.
Frame vibration
Vibration is monitored on the bogie frame to detect changes in mechanical behavior, imbalance or component deterioration.
Signal health
Bogie monitoring validates signal quality and node status before data is forwarded to the local analytics infrastructure.
Feature to benefit
From bogie signals to maintenance decisions
Multi-sensor acquisition
Monitor critical bogie components continuously.
Edge filtering
Reduce noise and avoid unnecessary data traffic.
Secure gateway
Protect operational railway data between vehicle and server.
Local AI integration
Detect anomalies and support maintenance decisions.
How it works
From sensor to decision in four steps
Measure
Temperature and vibration are collected from critical bogie points.
Process locally
Bogie monitoring validates and filters data directly on the vehicle.
Secure transfer
Only relevant information reaches the local infrastructure.
Detect anomalies
Local AI identifies patterns, trends and abnormal behavior.
Local intelligence
AI that runs on your server, not in the cloud
Data never leaves the operator's infrastructure. AI models interpret processed signals and provide actionable insights for maintenance teams.
Detect abnormal temperature evolution
spot early thermal changes before maintenance thresholds are reached
Identify vibration deviations
track changes that may indicate imbalance, play or component wear
Correlate thermal and vibration behavior
turn combined sensor data into maintenance-relevant signals
Support alerts and reporting
provide local reports for maintenance planning and fleet diagnostics
Typical applications
Designed for railway monitoring programs
Why local processing
Built for operators who need control over operational data
Cloud approach
Raw data transfer
External hosting
Higher bandwidth
Potential data sovereignty concerns
Bogie monitoring local approach
Local filtering
Customer infrastructure
Reduced traffic
Local control of operational data
Explore a pilot deployment
Discuss monitoring architecture, sensor placement and local AI integration
We can walk through the architecture, review bogie measurement points, and define how Bogie monitoring connects to your local maintenance analytics environment.