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基于改进的LSTM-AE算法和TSO-VMD无声化技术的电梯故障前体预测.

Hao Cao1, Xiaoyan Du2

  • 1School of Architecture and Engineering, Xuchang Vocational and Technical College, China.

PloS one
|April 24, 2025
PubMed
概括

一个新的VMD-BILSTM-AEAM算法通过减少操作数据中的噪音和冗余来增强电梯故障预测. 这种先进的方法提高了预测性维护和故障检测系统的准确性.

科学领域:

  • 工程 工程师 工程师 工程师
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 电梯运行数据经常受到特征冗余和噪声的影响,阻碍了准确的故障预测.
  • 传统的方法难以应对电梯实时数据的复杂性和数量.
  • 预测性维护对于确保电梯安全和运营效率至关重要.

研究的目的:

  • 开发一种先进的算法来预测电梯故障,使用深度学习和信号处理技术的新组合.
  • 为应对电梯运行数据中数据噪声和特征冗余的挑战.
  • 提高电梯故障前体预测的准确性和稳定性.

主要方法:

  • 拟议的VMD-BILSTM-AEAM算法集成了变化模式分解 (VMD),双向长期短期记忆 (BILSTM) 和带有注意力机制的自动编码器 (AEAM).
  • 用于有效的特征选择,使用了属性相关密度排名 (ACDR).
  • 运营商优化的VMD被用于数据拒绝,以提高数据质量.

主要成果:

  • 该VMD-BILSTM-AEAM算法实现了0.919 (95%CI:0.915-0.924) 的平均真实阳性率 (TPR).
  • 平均虚假阳性率 (FPR) 为0.090 (95% CI:0.087-0.092),平均曲线下面积 (AUC) 为0.919 (95% CI:0.915-0.923),记录下来.

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  • 绩效指标显示,与传统和其他深度学习模型相比,表现有显著改善.

    • 结论:

    • VMD-BILSTM-AEAM算法为电梯故障前体预测提供了卓越的准确性和稳定性.
    • 该模型有效地处理杂的时间序列数据,展示了它对更广泛的预测性维护应用的潜力.
    • 这种先进的方法通过改进故障检测来提高电梯系统的可靠性和安全性.