Standalone rail-temperature measurement under live operating conditions, turned into real-time dashboards and alerts before thermal drift becomes a problem.
EdgeRaspberry Pi nodes
TransportMQTT
Alertingthreshold rules
Role
Edge hardware, data pipeline and supervision
Context
MESEA · high-speed line
Stack
Raspberry Pi
Python
MQTT
Prometheus
Grafana
Linux (Debian)
01Rail sensorstemperature
02Edge nodeRaspberry Pi · Python
03MQTT brokercentral
04Prometheusstorage · rules
05Grafanadashboards · alerts
Fig. 03.1 — Measurement chain
Why it matters
Rail temperature is a key input for track maintenance: large swings put continuously welded rail under thermal stress. Maintenance teams need trends and thresholds, not occasional readings taken by hand.
Edge acquisition
Raspberry Pi edge nodes with temperature sensors, installed and running on a live line.
Measurements published over MQTT; collectors buffer locally and reconnect on their own when the link drops.
Supervision
Central broker feeding Prometheus, with Grafana dashboards for live values and trends.
Threshold alerts for thermal drift instead of manual checks after the fact.
The infrastructure itself is monitored too, so a silent sensor is noticed as quickly as a hot rail.