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  3. Ri2ap: Robust And Interpretable 2d Anomaly Prediction In Assembly Pipelines.
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  3. Ri2ap: Robust And Interpretable 2d Anomaly Prediction In Assembly Pipelines.

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RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.

Chathurangi Shyalika1, Kaushik Roy1, Renjith Prasad1

  • 1Artificial Intelligence Institute, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA.

Sensors (Basel, Switzerland)
|May 25, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

A new method, Robust and Interpretable 2D Anomaly Prediction (RI2AP), enhances anomaly prediction in manufacturing assembly lines. This approach significantly improves F1 scores, offering valuable insights for industrial processes.

Keywords:
anomaly predictionassembly processessensor datasmart manufacturingtime series analysis

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Area of Science:

  • Manufacturing
  • Industrial Engineering
  • Machine Learning

Background:

  • Anomaly prediction is vital for manufacturing efficiency and cost reduction.
  • Current machine learning methods struggle with rare events and complex dependencies, yielding suboptimal F1 scores.
  • High-fidelity simulation data is scarce and expensive, limiting traditional ML model training.

Purpose of the Study:

  • To introduce a novel method, Robust and Interpretable 2D Anomaly Prediction (RI2AP), for enhanced anomaly prediction.
  • To address the dual challenges of predicting anomaly occurrence and understanding inter-dependencies.
  • To provide interpretable insights into sensor data for domain experts.

Main Methods:

  • Developed Robust and Interpretable 2D Anomaly Prediction (RI2AP).
  • Utilized a causal-influence framework for model interpretability.
  • Validated on rocket assembly simulations and real-world manufacturing data.

    • Main Results:

    • RI2AP demonstrated up to a 30-point F1 measure improvement over existing ML methods.
    • The method effectively predicts anomaly occurrences and their dependencies.
    • The interpretation mechanism provided actionable insights to domain experts.

    Conclusions:

    • RI2AP significantly advances anomaly prediction in manufacturing assembly lines.
    • The model's interpretability enhances trust and utility for industrial applications.
    • RI2AP shows strong potential for real-world deployment in complex manufacturing settings.