Master thesis - Data analysis and Machine learning

A thesis project at Munters to develop a data-driven predictive maintenance proof-of-concept for industrial dehumidifiers. The work uses existing sensor and controller data to analyze signals and degradation patterns and to build and evaluate an ML/anomaly-detection model for a critical component. Deliverables include prototype code, quantitative evaluation, a written thesis, and recommendations for scaling to a full predictive maintenance solution.