基于时空图注意网络的磨煤机故障预警

牛玉广; 徐健; 杜鸣; 姚珺

doi:10.19805/j.cnki.jcspe.2025.240423

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基于时空图注意网络的磨煤机故障预警

数字化与智能化 | 更新时间：2026-10-10

- 基于时空图注意网络的磨煤机故障预警
- Spatio-temporal Graph Attention Network-based Fault Warning of Coal Mill
- 动力工程学报 2025年45卷第9期页码：1519-1526
- 作者机构：
  
  1. 华北电力大学新能源电力系统全国重点实验室,北京,102206
  2. 华北电力大学控制与计算机工程学院,北京,102206
  3. 北京怀柔实验室,北京,101400
- 作者简介：
  
  牛玉广(1964—),男,河南安阳人,教授,博士,研究方向为新能源电力系统建模与控制、大型火电机组优化控制与故障诊断
  徐健(通信作者),男,博士研究生,E-mail:ncepu_zdsxj@163.com
- 基金信息：
  
  国家自然科学基金资助项目(52206009);中央高校基本科研业务费面上资助项目(2024MS018)
- DOI：10.19805/j.cnki.jcspe.2025.240423
  中图分类号：
- 网络首发：2025-09-16，
  
  纸质出版：2025
- 稿件说明：
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牛玉广,徐健,杜鸣,姚珺. 基于时空图注意网络的磨煤机故障预警动力工程学报, 2025, 45(9): 1519-1526 https://doi.

org/10.19805/j.cnki.jcspe.2025.240423
牛玉广,徐健,杜鸣,姚珺. 基于时空图注意网络的磨煤机故障预警动力工程学报, 2025, 45(9): 1519-1526 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240423.

org/10.19805/j.cnki.jcspe.2025.240423 DOI：

摘要

为提升磨煤机故障预警结果的准确性与可信度

提出一种基于时空图注意网络的故障预警方法。通过最大信息系数与Top-K最近邻方法自适应求取邻接矩阵

将原始列表数据重构为时序图数据。随后

依次使用图注意力网络和双向门控循环单元

分别提取图数据的空间特征和时间特征

并对下一时刻数据进行预测。在离线阶段

经指数加权移动平均法计算总体预警阈值与各分量阈值。在在线阶段

当预测残差总量越限时发出预警信号

同时绘制各分量的越限分数热力图。结果表明:以某热电机组中速磨煤机运行数据为例

所提方法能够准确预警设备潜在异常

并对预警原因进行有效解释

优于对比方法。

Abstract

To improve the accuracy and reliability of fault warning for coal mills

a spatio-temporal graph attention network-based fault warning method was proposed. The maximal information coefficient and Top-K nearest neighbours were used to obtain the adjacency matrix adaptively. The original list data were reconstructed into sequence graph data. The spatial and temporal features of the graph data were extracted using the graph attention network and bidirectional gate recurrent unit in turn

and the next time data were predicted. In the offline stage

the overall warning threshold and each component threshold were calculated by the exponential weighted moving average method. In the online stage

a warning signal was issued when the total amount of prediction residuals exceeded the limit

meanwhile the heat map of the exceedance score of each component was plotted. Results show that taking the operation data of the medium-speed coal mill in a cogeneration unit as an example

the proposed method can accurately warn the potential anomalies of the equipment and effectively explain the reasons for the warning

which is better than relevant comparison methods.

关键词

Keywords

references

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