Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD

TL;DR

This paper demonstrates the application of the AcME-AD framework in industrial settings, providing fast, flexible, and user-friendly explanations for anomaly detection to enhance trust and decision-making in Industry 5.0.

Contribution

It presents the first industrial application of AcME-AD, showcasing its effectiveness for explainable anomaly detection and root cause analysis in real-world industrial environments.

Findings

AcME-AD offers real-time, model-agnostic explanations for anomalies.

The framework improves trust and interpretability in industrial anomaly detection.

Experimental results confirm AcME-AD's suitability for industrial decision support.

Abstract

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.