Building software for clients since 2010
- 15+
- Years shipping software
- 84%
- Retainer renewal rate
- 4–8
- Weeks to first production ship
AI · Manufacturing
The making of Predictive Maintenance Platform
ML-powered predictive maintenance system using LightGBM on SCADA/IoT sensor data with weekly automated retraining — reduced unplanned downtime by 31% in 90 days.
- Client
- Industrial Client
- Timeline
- 10 weeks
- Built with
- Python, LightGBM, scikit-learn, Airflow, PostgreSQL
Challenges
- Handling noisy and missing sensor data
- Achieving low false-positive rates
- Integrating with legacy industrial systems
Solution
We built an ML-powered predictive maintenance system that analyzes SCADA and IoT sensor data to predict equipment failures before they occur. The system uses LightGBM with automated weekly retraining to continuously improve prediction accuracy.
Services provided
Colors
#262D36
#3B444D
#C0A8B0
#8F7071
What we built
Real-Time Sensor Data Ingestion
High-throughput pipeline processing thousands of SCADA and IoT sensor readings per second with minimal latency.
LightGBM Prediction Models
Gradient-boosted decision trees trained on historical failure patterns to predict equipment breakdowns days in advance.
Automated Weekly Retraining
Self-improving ML pipeline that retrains models weekly on fresh data, ensuring predictions stay accurate over time.
Alert Dashboard for Maintenance Teams
Real-time monitoring dashboard with severity-ranked alerts, equipment health scores, and recommended actions.
Historical Failure Analysis
Deep-dive analytics into past equipment failures with root cause patterns and mean-time-between-failure trends.
SCADA System Integration
Seamless connection with existing industrial control systems through standard protocols without disrupting operations.
Results
31%
reduction in unplanned downtime
ROI achieved within 90 days
95%
prediction accuracy on critical failures
