Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.

翻译：可解释人工智能（XAI）作为关键接口，促进数据科学家、领域专家、终端用户等多元群体与复杂智能系统间的交互。它有助于解读机器学习中"黑箱"模型的复杂内在机理，使模型决策依据更易于理解。然而，当前XAI研究主要聚焦于两大方向：提升用户信任度的方法，或对机器学习模型进行调试与优化。但大多数研究未能充分认识到更广泛场景中所需解释类型的多样性——不同用户群体与多样化应用领域需要量身定制的解决方案。预测性维护（PdM）正是这样的领域之一，作为工业4.0与5.0框架下蓬勃发展的研究方向，本立场论文揭示了现有XAI方法论与工业应用（特别是预测性维护领域）中对解释的特定需求之间的鸿沟。尽管可解释性至关重要，该课题仍属相对未被充分探索的领域，本文作为先驱性尝试，旨在将相关研究挑战引入学界视野。我们概述了预测性维护任务，强调对应解释的必要性与多元目的，进而梳理评述文献中常用的XAI技术，探讨其在PdM任务中的适用性。最后，为使观点与论断更具象化，我们展示了XAI在商用车、地铁列车、钢铁厂、风电场四类工业场景中的应用案例，并指出亟待深入研究的领域方向。