Manufacturing industries are increasingly adopting data-driven, decision making systems towards the Industry 4.0 paradigm. In the context of this data revolution, the innovative SiMoDiM project aims at developing a smart predictive maintenance system for the stainless steel industry. In its first stage, it focuses on the assets within the hot rolling process, one of the core components involved in the manufacturing of steel sheets, and more specifically on the coiler drums of Steckel mills. These drums operate under mechanical and thermal stresses that degrade them, and their replacements directly impact the product valor chain. In this work we present the data analysis stage of SiMoDiM, where the huge amount of available historical and real-time data from the hot rolling process (collected by onboard sensors in the mills) are studied in order to find which variables and descriptors are valid indicators of the coiler drums' conditions. This analysis is the first step towards an intelligent system that takes advantage of such descriptions for performing a predictive maintenance of the machinery.
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