Anomaly detection (AD) is a crucial process often required in industrial settings. Anomalies can signal underlying issues within a system, prompting further investigation. Industrial processes aim to streamline operations as much as possible, encompassing the production of the final product, making AD an essential mean to reach this goal.Conventional anomaly detection methodologies typically classify observations as either normal or anomalous without providing insight into the reasons behind these classifications.Consequently, in light of the emergence of Industry 5.0, a more desirable approach involves providing interpretable outcomes, enabling users to understand the rationale behind the results.This paper presents the first industrial application of ExIFFI, a recently developed approach focused on the production of fast and efficient explanations for the Extended Isolation Forest (EIF) Anomaly detection method. ExIFFI is tested on two publicly available industrial datasets demonstrating superior effectiveness in explanations and computational efficiency with the respect to other state-of-the-art explainable AD models.

该论文介绍了一种名为ExIFFI的方法，在工业领域首次应用扩展隔离森林（EIF）异常检测方法，通过提供解释性输出来获得理解结果的原理，并在两个公开工业数据集上进行测试，展示了在解释和计算效率上相对于其他最先进的可解释AD模型的卓越效果。

可解释的基于数据驱动的工业过程异常检测