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Fault subspace decomposition and reconstruction theory based online fault prognosis

机译:基于在线故障预测的故障子空间分解与重构理论

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摘要

In this paper, a monitoring-statistic-based fault subspace decomposition and double decomposition (DD) reconstruction method is presented to deal with multivariable continuous slow-varying process fault prognosis. The new method assumed that fault is known and can be completely reconstructed. Then, the fault directions are determined by the monitoring-statistic-based fault subspace decomposition method. Finally, the DD reconstruction method and the vector autoregression (VAR) method are used to calculate and predict the magnitude degeneration process of corresponding fault directions. After that, online fault prognosis is realized. The experiments show the effectiveness of the developed method.
机译:提出了一种基于监测统计的故障子空间分解与双分解(DD)重构方法,以处理多变量连续慢变过程的故障预测。新方法假定故障是已知的并且可以完全重建。然后,通过基于监控统计的故障子空间分解方法确定故障方向。最后,利用DD重建方法和矢量自回归(VAR)方法来计算和预测相应故障方向的幅度退化过程。之后,实现在线故障诊断。实验证明了该方法的有效性。

著录项

  • 来源
    《Control Engineering Practice》 |2019年第4期|121-131|共11页
  • 作者单位

    Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China;

    Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China;

    Shanghai Ship & Shipping Res Inst, State Key Lab Nav & Safety Technol, Shanghai 200135, Peoples R China;

    Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    PCA; Fault subspace decomposition; Fault reconstruction; Prognosis; Vector autoregression;

    机译:PCA;故障子空间分解;故障重构;预测;向量自回归;

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