Abstract
In today's competitive manufacturing landscape, minimizing downtime and optimizing equipment performance is critical. Predictive maintenance (PdM) emerges as a powerful tool, utilizing machine learning (ML) algorithms to anticipate equipment failures before they occur. This article delves into the world of ML algorithms for PdM in manufacturing, exploring their functionalities, strengths, and weaknesses. We examine prominent algorithms like regression, anomaly detection, and survival analysis, highlighting their suitability for different maintenance scenarios. By understanding the capabilities and limitations of these algorithms, manufacturing professionals can choose the right tool for the job, ensuring robust and reliable PdM systems.