Predictive Maintenance Platform
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.
Client
Industrial Client
Industry
Manufacturing
Duration
10 weeks
Tech Stack
Python, LightGBM, scikit-learn, Airflow, PostgreSQL
What We Built
Key Features
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.
The Challenge
Problems We Solved
- Handling noisy and missing sensor data
- Achieving low false-positive rates
- Integrating with legacy industrial systems
The Outcome
Results Achieved
- 31% reduction in unplanned downtime
- ROI achieved within 90 days
- 95% prediction accuracy on critical failures
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