  Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0,
and became crucial for enhancing operational efficiency, allowing to minimize
downtime, extend lifespan of equipment, and prevent failures. A wide range of
PdM tasks can be performed using Artificial Intelligence (AI) methods, which
often use data generated from industrial sensors. The steel industry, which is
an important branch of the global economy, is one of the potential
beneficiaries of this trend, given its large environmental footprint, the
globalized nature of the market, and the demanding working conditions. This
survey synthesizes the current state of knowledge in the field of AI-based PdM
within the steel industry and is addressed to researchers and practitioners. We
identified 219 articles related to this topic and formulated five research
questions, allowing us to gain a global perspective on current trends and the
main research gaps. We examined equipment and facilities subjected to PdM,
determined common PdM approaches, and identified trends in the AI methods used
to develop these solutions. We explored the characteristics of the data used in
the surveyed articles and assessed the practical implications of the research
presented there. Most of the research focuses on the blast furnace or hot
rolling, using data from industrial sensors. Current trends show increasing
interest in the domain, especially in the use of deep learning. The main
challenges include implementing the proposed methods in a production
environment, incorporating them into maintenance plans, and enhancing the
accessibility and reproducibility of the research.
