Smart Manufacturing with IoT
Predictive maintenance and real-time monitoring reducing downtime by 45%
Project overview
TechManufacturing Corp operates 5 production facilities with over 200 industrial machines. Unplanned downtime was costing $500K+ monthly, and maintenance was purely reactive. They needed a predictive maintenance system to optimize operations and minimize costly equipment failures.
Pious Dove designed and deployed an end-to-end IoT platform with 500+ sensors monitoring temperature, vibration, pressure, and power consumption across all machines. The system uses machine learning to predict failures 2-4 weeks in advance, enabling proactive maintenance scheduling.
The challenge
Legacy Equipment: Machines from different eras with no digital interfaces
Data Integration: Siloed systems across 5 facilities with no unified view
Network Connectivity: Industrial environments with harsh conditions and limited WiFi
Real-time Processing: Need to process 10M+ data points daily for immediate insights
Accuracy Requirements: False positives costly, false negatives catastrophic
Our solution
Sensor Network: Industrial-grade sensors with LoRaWAN connectivity for harsh environments
Edge Computing: Local gateways for real-time processing and reduced latency
Cloud Platform: AWS IoT Core with time-series database (TimescaleDB)
ML Models: Custom anomaly detection and predictive models trained on historical failure data
Dashboard & Alerts: Real-time monitoring with mobile alerts for maintenance teams
Integration: Connected to existing CMMS and ERP systems
Measurable results
Reduction in unplanned downtime
Annual cost savings in maintenance
Increase in equipment lifespan
Prediction accuracy for failures
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