Predictive Maintenance
IoT-connected system for manufacturing that predicts equipment failure before it happens, scheduling auto-repairs.
Services
IoT, Time-Series Analysis
Stack
AWS IoT Core, Python, Grafana
Timeline
6 Weeks
Client
Automotive Plant
The Challenge
Unplanned downtime on the assembly line was costing the factory over $50,000 per hour. Maintenance was schedule-based (every 3 months), meaning machines were often serviced too early or too late.
The Solution
We installed IoT vibration and temperature sensors on key robotic arms, feeding data into a predictive model.
- Failure Prediction: The AI identifies micro-vibrations that precede bearing failure by 48 hours.
- Auto-Ticketing: Automatically generates work orders in the maintenance system only when repairs are actually needed.
- Supply Chain Sync: Pre-orders spare parts if inventory is low when a potential failure is detected.
The Impact
Downtime was slashed by 30%, and the life of expensive machinery was extended by servicing it exactly when required.