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

Valentina Zaccaria, Chiara Masiero, David Dandolo, Gian Antonio Susto
4/29/2024
cs.LG

No official code found. Best implementation: dandolodavid/ACME (10 stars, last commit 2024-08-07).

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.

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Code Implementations(1)

dandolodavid/ACMECommunity100%

ACME - Accelerated Model Explainability

103HTML, PythonJul 30, 20202 years ago

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Cite this paper

@article{zaccaria2024enabling,
  title  = {Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD},
  author = {Valentina Zaccaria and Chiara Masiero and David Dandolo and Gian Antonio Susto},
  year   = {2024},
  eprint = {2404.18525},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.18525v2}
}

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