解决异质时间频率因果关系:探索工业根源原因对齐和诊断的源源一致性
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了一个因果源一致性分析 (CSCA) 框架,以解决根源诊断中的因果异质性. CSCA确保在时间和频率领域一致识别故障来源,以改进过程监控.
科学领域:
- 过程监控和控制过程的监控和控制.
- 数据驱动的故障诊断数据驱动的故障诊断
- 复杂系统中的因果推理.
背景情况:
- 过程变量表现出时间趋势和周期性,导致不同的时间域和频率域因果关系.
- 这种因果异质性挑战了准确的根本原因诊断 (RCD).
- 现有的方法很难调和不同的因果关系观点.
研究的目的:
- 提出一个新的框架,因果源一致性分析 (CSCA),用于强大的RCD.
- 在格兰杰因果关系 (GC) 分析中实现时间频率协同作用.
- 克服故障诊断中的因果异质性挑战.
主要方法:
- 开发了一个非线性增强模块用于时空特征提取.
- 设计了一个并行因果关系学习模块,用于时间域和频域GC的可分化的频域扩展.
- 包含了因果意义和稀疏性的时间频率约束.
- 提出了一个根源原因对齐模块,使用自身值分解和近似指数转换来实现端到端源的一致性.
主要成果:
- 该CSCA框架成功地确定了田纳西东曼工艺和燃气轮机应用中的根本原因.
- 证明CSCA通过在各个领域实现一致的根源原因识别来克服因果异质性.
- 废弃性研究验证了时间频率协同方法的有效性.
结论:
- 通过确保时间域和频域因果关系之间的源一致性,CSCA为RCD提供了统一的方法.
- 该框架有效地解决了传统方法面临因果异质性的局限性.
- 在工业过程中,CSCA为可靠的故障诊断提供了一个有前途的解决方案.
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