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CASE 02Industrial IoT & Manufacturing · Industrial Manufacturing Co

Predictive Maintenance AI — Real-Time IoT Telemetry & ML Pipeline

High-throughput ML pipeline predicting industrial hardware failure

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Performance Impact
+85%
Business Growth
+85%
Time to Production
8 weeks
Predictive Maintenance AI — Real-Time IoT Telemetry & ML Pipeline
AIVERIFIED PRODUCTION
The Legacy Challenge

What the client was facing

Unscheduled equipment breakdowns causing millions of dollars in factory downtime and reactive, high-cost emergency part replacements.

Architectural Solution

What NemeaForge Engineered

Engineered an edge-to-cloud telemetry ingestion pipeline feeding machine learning models that detect vibration and thermal anomalies ahead of failure.

Key System Highlights
  • Apache Kafka event bus ingesting 50,000+ sensor signals/sec
  • Time-series anomaly detection models trained on historical failure logs
  • Sub-50ms inference API served with FastAPI and Redis cache
  • Automated preventive maintenance ticket dispatching system
Measurable Outcomes

Business & Technical Impact

  • 85% precision in predicting mechanical failures 72 hours in advance
  • 60% decrease in unplanned assembly line downtime
  • 40% reduction in emergency replacement parts expenditure
Technologies Utilized
PythonPyTorchApache KafkaFastAPITimescaleDBAzure ML
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