Predictive maintenance (PdM) enhances industrial efficiency by reducing downtime and optimizing maintenance through data-driven insights. This thesis evaluates three predictive maintenance platforms, ABB Genix, Iba Analyzer, and Microsoft Power BI, through hands-on testing and feature comparison, focusing on data integration, predictive analytics, real-time condition monitoring, automated alerts, and dashboard customization. 

ABB Genix excelled in statistical-based and condition-based predictive maintenance analytics, leveraging AI-driven concepts and ARIMA modeling. Iba Analyzer supported condition-based predictive maintenance and data acquisition but lacked built-in machine learning. Power BI demonstrated flexibility, integrating a KMeans clustering model developed in Jupyter Notebook.

Results highlight the strength of each platform, offering recommendations for selecting predictive maintenance solutions in industrial settings. These findings support data-driven decision-making, aligning with Industry 5.0 goals of sustainability and efficiency.