Industrial IoT Telemetry & Predictive Maintenance
Real-time edge sensor ingestion pipeline and anomaly detection for manufacturing facilities.
System Context & Operational Scope
An end-to-end telemetry system capturing vibration, temperature, and acoustic metrics across factory machinery to predict mechanical failures before downtime occurs.
Unplanned manufacturing machine stoppages were costing production facilities thousands of dollars per hour. Existing supervisory control systems lacked predictive foresight.
Core Engineering Objectives
- Ingest 50,000+ sensor data points per second with sub-50ms latency.
- Detect early micro-vibration deviations indicative of bearing wear.
- Deliver an intuitive real-time operations dashboard for floor managers.
Verified Performance Metrics
Technologies Used
Architectural Strategy
Engineered a low-footprint C++ firmware client for edge gateways reading industrial Modbus/CAN bus registers, communicating via MQTT over TLS to a resilient Go ingestion cluster backed by Apache Kafka.
Implementation Details
A distributed streaming architecture feeding TimescaleDB for time-series aggregation and an unsupervised anomaly detection model triggering automated maintenance alerts.
Capabilities
Key Architecture Features
Specific technical capabilities built into this platform to ensure reliability and performance.
High-throughput MQTT broker cluster handling 50k+ events/sec
Dynamic thresholding and FFT spectral analysis at the edge
Interactive SVG floorplan visualization with live equipment status
Automated SMS/Email incident dispatch and technician ticketing
Historical degradation trend analysis and remaining useful life (RUL) estimation
Eliminated 62% of unplanned machinery downtime across 4 pilot production facilities within the first 6 months of operation.
Have a Technical Project in Mind?
Discuss your requirements with our architects. We will provide practical guidance on tech stacks, timelines, and implementation feasibility.