Predictive Maintenance for Manufacturing Plants
An IoT and AI platform that helps manufacturers detect equipment anomalies earlier and prioritize maintenance work.
The challenge
NovaForge relied heavily on reactive maintenance. Sensor data existed, but teams lacked a reliable way to detect anomalies early and prioritize interventions before production stopped.
Our solution
Electro AI Lab connected plant sensors to an analytics and alerting platform that detects unusual equipment behavior and presents prioritized maintenance recommendations.
Our approach
We validated device connectivity and signal quality first, then built anomaly detection and an operations dashboard that maintenance leads could trust during plant-floor decision making.
Key features
- Sensor ingestion
- Anomaly detection
- Maintenance priority queues
- Plant operations dashboard
Technologies used
Technical architecture
Devices publish signals over MQTT into a cloud ingestion layer. Python models evaluate anomalies, while a React dashboard presents prioritized maintenance queues backed by PostgreSQL on AWS.
Development process
- 1Sensor and connectivity assessment
- 2Ingestion and data quality setup
- 3Anomaly model development
- 4Operations UI and pilot tuning
Outcomes
Visibility into equipment anomalies
Maintenance queues for plant teams
Project gallery
Frequently asked questions
Is this a full digital twin system?
No. This case study focuses on sensor-driven anomaly detection and maintenance prioritization rather than a complete digital twin.
What industries can use a similar approach?
Manufacturers and industrial operators with reliable sensor telemetry and a need for earlier maintenance visibility.
Related case studies
Ready to Transform Your Business?
Partner with Electro AI Lab to build AI-powered software and technology solutions that drive real business growth.