From Data to Decisions: AdaBoost-based Approach for Remaining useful Life Estimation

In today's industrial environment, the proactive management of assets has a crucial role in ensuring operational efficiency and mitigating unforeseen downtime. This proactive approach is based on the ability to accurately estimate the Remaining Useful Life (RUL) of critical components and systems. This paper overviews the known approaches to support decision making in maintenance. The study involves the analysis of operational data, utilizing predictive models to forecast the remaining lifespan of critical assets. An AdaBoost-based prediction approach is presented. The integration of machine data-driven RUL predictions promises to revolutionize asset management practices, providing industries with a powerful tool to mitigate downtime and improve resource allocation.