Predictive Maintenance for Steel Production Equipment
Area: Predictive Analytics & Machine Learning
Description: Built a predictive model to forecast the operational cycle of steel production equipment using historical data. Employed machine learning techniques to achieve an 82% accuracy rate. The model was integrated into a GUI developed using Dash, allowing for real-time predictions and maintenance scheduling, reducing unexpected downtimes.
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Posted Aug 17, 2024
Developed an 82% accurate machine learning model to forecast steel equipment cycles, minimizing downtime with real-time Dash visualizations.
Real-Time Production Data Visualization
ETL Pipeline Development
E&P Data Management Solution Deployment