Real-Time Facility Monitoring Network

The challenge

A multi-site industrial operator had zero real-time visibility into equipment health across 4 facilities. Maintenance was entirely reactive — technicians only discovered failures after they caused production downtime, costing an average of $12,000 per incident in lost output.

Existing monitoring relied on daily manual inspections and paper-based checklists. Critical equipment like compressors, HVAC units, and conveyor systems had no continuous telemetry. By the time anomalies were noticed, the damage was already done.

Our approach

Designed and deployed a network of 48 IoT sensors across all facilities using Raspberry Pi edge nodes. Each node collects vibration, temperature, humidity, and power draw data at 5-second intervals.

Built an edge computing layer that performs local anomaly detection using statistical thresholds, reducing cloud bandwidth by 85% while enabling sub-second alerting for critical events.

Created a centralised operations dashboard aggregating all facility data with trend analysis, predictive maintenance scoring, and automated alert routing to the right maintenance teams.

Results

60% — faster fault detection

48 — sensors deployed across 4 sites

$180K — estimated annual savings

Stack

Raspberry Pi, MQTT, Node.js, PostgreSQL