While Machine Learning has become crucial for Industry 4.0, its opaque nature
hinders trust and impedes the transformation of valuable insights into
actionable decision, a challenge exacerbated in the evolving Industry 5.0 with
its human-centric focus. This paper addresses this need by testing the
applicability of AcME-AD in industrial settings. This recently developed
framework facilitates fast and user-friendly explanations for anomaly
detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes
real-time efficiency. Thus, it seems suitable for seamless integration with
industrial Decision Support Systems. We present the first industrial
application of AcME-AD, showcasing its effectiveness through experiments. These
tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and
feature-based root cause analysis within industrial environments, paving the
way for trustworthy and actionable insights in the age of Industry 5.0.

本文首次在工业环境中应用 AcME-AD，实验证明其作为工业环境下可信且可操作洞察力的有价值工具，通过提供模型无关且灵活的快速用户友好解释，以及基于特征的根本原因分析，为支持行动决策提供支持，为透明度不足、洞察力无法转化为可操作决策的挑战提供解决方案，特别适用于与人为中心的演进工业时代。