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Poster: Human-in-the-Loop Anomaly Detection in Industrial Data Streams

Published: 20 September 2023 Publication History

Abstract

The detection of anomalies in an industrial setting remains an important and open challenge for most manufacturing companies. The potential benefits from the utilization of an anomaly detection system are substantial, as deviations from normal operating conditions can cause downtimes, quailty issues or safety hazards. The main requirements for an anomaly detection system include the selection of the machine learning model applicable to streaming data, providing the explanations of the model’s decision and participation of human operator in the learning process of the model. We have proposed the anomaly detection system, which addresses the above challenges and is applicable in industrial environment.

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Cited By

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  • (2024)Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learningStructural Health Monitoring10.1177/14759217241242031Online publication date: 9-Apr-2024

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  1. Poster: Human-in-the-Loop Anomaly Detection in Industrial Data Streams

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      cover image ACM Other conferences
      CHItaly '23: Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter
      September 2023
      416 pages
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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 20 September 2023

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      Author Tags

      1. anomaly detection
      2. data streams
      3. explainable artificial intelligence

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      • National Science Centre Poland

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      CHItaly 2023

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      Overall Acceptance Rate 109 of 242 submissions, 45%

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      • (2024)Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learningStructural Health Monitoring10.1177/14759217241242031Online publication date: 9-Apr-2024

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