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ICAS2020_0467_paper.pdf
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🔗 Source: icas.org
📊 Size: 540 KB
📄 Pages: 14 pages
⬇️ Downloads: 148

Summary

Researchers from the Aerospace Systems Design Laboratory developed a supervised anomaly detection model with automatically engineered features for bearing health monitoring. The model utilizes time-domain features and a neural network architecture to learn feature significance and optimize weights for anomaly classification. Bayesian optimization and the Asynchronous Successive Halving Algorithm (ASHA) are combined for efficient hyperparameter tuning. The methodology is demonstrated on a candidate dataset and is expected to be valuable for practitioners addressing anomaly detection problems in various industries.

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Description

Researchers from the Aerospace Systems Design Laboratory developed a supervised anomaly detection model with automatically engineered features for bearing...

Technical Information

  • File Format: PDF
  • File Size: 540 KB
  • Pages: 14
  • Language: EN
  • Total Downloads: 148
  • Last Updated: 1 week ago

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