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.

翻译：预测性维护作为工业4.0的支柱之一，对于提高运营效率、减少停机时间、延长设备寿命和防止故障至关重要。基于人工智能的方法可执行多种预测性维护任务，这些方法通常利用工业传感器生成的数据。钢铁工业作为全球经济的重要分支，因其巨大的环境足迹、市场全球化的特点以及严苛的工作条件，成为这一趋势的潜在受益者之一。本综述综合了钢铁行业基于人工智能的预测性维护领域的当前知识现状，面向研究人员和从业者。我们识别了与该主题相关的219篇文章，并提出了五个研究问题，从而获得对当前趋势和主要研究空白的全局视角。我们审视了受预测性维护影响的设备和设施，确定了常见的预测性维护方法，并识别了开发这些解决方案所使用的人工智能方法的趋势。我们探讨了所综述文章中使用的数据特征，并评估了其中呈现的研究的实践意义。多数研究聚焦于高炉或热轧工序，采用工业传感器数据。当前趋势显示该领域兴趣日益增长，尤其是深度学习的使用。主要挑战包括在实际生产环境中实施所提出的方法、将其纳入维护计划，以及增强研究的可获取性和可重复性。