Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 31 references that are in the map. First posted on arXiv 29 Apr 2024.
Builds on
6 of 31 references.
References · 6
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
- 2021An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinerycited by 22
- 2019Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detectioncited by 19
- 2021AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Boxcited by 8
- 2024AcME-AD: Accelerated Model Explanations for Anomaly Detectioncited by 2
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Citing papers per year: 2024: 2.
2 papers.