Predictive Maintenance Dashboard

I developed a machinery-data analysis and visualization application to inspect operating parameters and identify conditions associated with failures. I implemented processing and organization of records containing temperature, rotational speed, torque and tool wear, transforming them into indicators accessible through an interactive dashboard. The interface lets users explore records, visualize relationships between parameters, compare operating conditions and inspect analysis results. It includes charts, indicators and an operational-risk simulator to support interpretation. Development covered data preparation and processing, analytical logic and the visualization interface. Machine Learning models complement the system with estimates that support diagnosis and maintenance decisions. Portfolio date: August 2026.