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

• Main Authors: Zaccaria, Valentina, Masiero, Chiara, Dandolo, David, Susto, Gian Antonio
• Format: Journal Article
• Language: English
• Published: 29-04-2024
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2404.18525

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.