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An attribute reduction algorithm is proposed in order to promote the efficiency of fault detection of the flipping feeding machine. The optimized decision model is combined with decision tables and rough set to reduce the computational complexity. The reduction algorithm is enhanced by minimizing the decision risk and incorporating the probability threshold calculated based on Particle Swarm Optimization (PSO). The experiment results show that the proposed reduction algorithm can significantly improve the efficiency while ensuring the accuracy of fault prediction as well.

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The Attribute Reduction of Fault Prediction Dataset for the Flipping Feeding Machine Based on Rough Set Theory

  • 会议论文

  • 发表日期：

    2025年

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  • Xinyu Dai,
  • Lixin Lu,
  • Guiqin Li,
  • Peter Mitrouchev

摘要

An attribute reduction algorithm is proposed in order to promote the efficiency of fault detection of the flipping feeding machine. The optimized decision model is combined with decision tables and rough set to reduce the computational complexity. The reduction algorithm is enhanced by minimizing the decision risk and incorporating the probability threshold calculated based on Particle Swarm Optimization (PSO). The experiment results show that the proposed reduction algorithm can significantly improve the efficiency while ensuring the accuracy of fault prediction as well.