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

• URL: http://arxiv.org/abs/2404.18525v2
• Date: Fri, 25 Oct 2024 08:59:22 GMT
• Title: Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
• Authors: Valentina Zaccaria, Chiara Masiero, David Dandolo, Gian Antonio Susto,
• Abstract summary: We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments.
• Score: 5.315104943095396
• License:
• 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.

Related papers

• AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
  We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
  arXiv  Detail & Related papers  (2024-11-02T13:24:30Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey [9.088208602104105]
  Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry.
  arXiv  Detail & Related papers  (2024-05-21T13:32:46Z)
• Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI [3.7516053899419104]
  Industrial processes aim to streamline operations as much as possible, encompassing the production of the final product. In light of the emergence of Industry 5.0, a more desirable approach involves providing interpretable outcomes. 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.
  arXiv  Detail & Related papers  (2024-05-02T10:23:17Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• AcME-AD: Accelerated Model Explanations for Anomaly Detection [5.702288833888639]
  AcME-AD is a model-agnostic, efficient solution for interoperability. It offers local feature importance scores and a what-if analysis tool, shedding light on the factors contributing to each anomaly. This paper elucidates AcME-AD's foundation, its benefits over existing methods, and validates its effectiveness with tests on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2024-03-02T16:11:58Z)
• Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0 [9.903035948408316]
  We propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial in the model, thereby adding formal knowledge on smart manufacturing.
  arXiv  Detail & Related papers  (2023-07-13T13:52:41Z)
• MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy [68.7563053122698]
  We propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. We present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty.
  arXiv  Detail & Related papers  (2022-10-19T19:15:07Z)
• Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation [47.37985501848305]
  We introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection. This is complemented by insights and lessons learned during this early evaluation activity.
  arXiv  Detail & Related papers  (2022-07-10T18:51:12Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• Supporting Tool for The Transition of Existing Small and Medium Enterprises Towards Industry 4.0 [0.0]
  The main purpose of this work is to propose a methodology to support SMEs managers in better understanding the specific requirements for the implementation of Industry 4.0 solutions. A proposed methodology will be helpful for SMEs manager to take a decision regarding when and how to migrate to Industry 4.0.
  arXiv  Detail & Related papers  (2020-10-15T15:57:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.