Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey
Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro, Sepideh Pashami, Szymon Bobek, Joao Gama, Grzegorz J Nalepa
TL;DR
This survey analyzes AI-driven predictive maintenance in the steel industry, surveying 219 papers to map the state of the art, identify trends, and pinpoint gaps. It finds that neural networks and other ML methods predominate diagnostics-focused PdM tasks, with deep learning rising notably after 2020, while data-sharing and reproducibility remain constrained by private industry data. The paper documents a strong focus on BF, HRM, and CCM, and notes limited attention to scheduling and broader business impact, though some studies report tangible benefits. Overall, the findings highlight high potential for PDm in steel but call for greater emphasis on interpretability, practical deployment, and open data to accelerate real-world adoption.
Abstract
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.