Manufacturing
Predictive Maintenance System for an Industrial Manufacturer
Shifting equipment maintenance from reactive to predictive using sensor data and machine learning models.
ManufacturingA mid-size industrial manufacturer
Client details anonymized for confidentiality.
The challenge
Equipment maintenance was purely reactive: machines ran until something failed, and unplanned downtime was a direct hit to production schedules.
- Unplanned downtime was the single largest source of production delays on the floor.
- Sensor data from equipment was being collected but never systematically analyzed.
- Maintenance teams had no reliable way to prioritize which machines needed attention first.
The solution
We built a predictive maintenance system that analyzes existing sensor data to flag equipment at risk of failure before it happens.
- Connected existing equipment sensor data into a centralized pipeline instead of leaving it siloed per machine.
- Trained models to flag early warning signs of failure specific to each equipment type.
- Built a maintenance dashboard that prioritizes machines by predicted risk, not just age or schedule.
Technologies
Tools we build with
PythonPyTorchKafkaPostgreSQLGrafanaAWS IoT
Results
The outcome
Maintenance shifted from a purely reactive schedule to one guided by actual equipment risk, catching problems before they caused downtime.
Reduced
Unplanned downtime
Reactive to predictive
Maintenance approach
Extended
Equipment lifespan
Client details anonymized for confidentiality. Figures reflect the representative outcomes this type of engagement targets.
Preview
Product screenshots
This project was built as part of our
AI DevelopmentProduction-grade AI systems, from LLM applications to RAG and agents.
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