基于注意力机制CNN-BiLSTM的磨煤机故障预警方法

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基于注意力机制CNN-BiLSTM的磨煤机故障预警方法

更新时间：2025-10-29

- 基于注意力机制CNN-BiLSTM的磨煤机故障预警方法
- Issue 4, Pages: 134-142(2025)
- 作者机构：
  
  1. 中国大唐集团科学技术研究院有限公司华东电力试验研究院
  2. 设备状态监测与控制教育部重点实验室(华北电力大学)
  3. 河北工程大学水利水电学院
  4. 安徽大学物质科学与信息技术研究院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM621;TP183
- Published：2025
- 稿件说明：
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[J]. 2025, (4): 134-142.
DOI：

[J]. 2025, (4): 134-142. DOI：

摘要

磨煤机是火电机组的重要辅机之一，其运行状态对机组的稳定出力和安全运行发挥着重要影响，所以研究故障预警方法可以有效降低磨煤机的故障停机概率，提升机组的整体可靠性。结合多种深度学习方法的优势，提出了基于注意力机制的卷积神经网络-双向长短期记忆神经网络(CNN-BiLSTM)的故障预警方法。通过CNN学习数据的空间特征，通过BiLSTM学习数据的时间特征，最后通过注意力机制为提取到的时空特征根据重要度分配不同的权重，增强重要特征在计算过程中的作用，从而提升模型的预测性能。使用某660 MW火电机组的磨煤机运行数据进行验证，结果表明所提方法在正常阶段相比对比方法具有更高的预测精度，在故障阶段预测残差的上升趋势更加明显。为了进一步提升故障预警效果，引入相对熵指标对残差进行处理，实验结果表明相对熵指标解决了残差控制图的缺点，有效提升了故障预警效果和预警时间。所提方法提前33 min预警了磨煤机轴承故障。

Abstract

The coal mill is one of the important auxiliary machines in thermal power units

and its operating status plays a significant role in the stable output and safe operation of the unit. Therefore

researching fault warning methods can effectively reduce the probability of unexpected shutdown of the coal mill and improve the overall reliability. By integrating the advantages of various deep learning methods

we propose a fault warning method based on attention mechanism CNN-BiLSTM. CNN is used to learn spatial features

and BiLSTM is used to learn temporal features. Finally

attention mechanism assigns different weights to the extracted spatiotemporal features based on their importance

thereby amplifying the influence of critical features during the computation process and elevating the model's predictive performance. A 660 MW thermal power unit's coal mill is used as a case study. The validation results show that the proposed method exhibits higher prediction accuracy than the comparison methods during the normal stage and a more pronounced upward trend in prediction residual during the fault stage. In order to further improve the performance of fault warning

we introduce the relative entropy index to process the residual. The experimental results show that the relative entropy index solves the shortcomings of the residual control chart and effectively improves the fault warning effectiveness and warning time. Specifically

the proposed method can issue a warning for coal mill bearing faults 33 minutes ahead of time.

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references

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