Hydraulic Turbine Cavitation State Recognition Method Based on Broad Convolutional Neural Network

LIU Zhong; ZHENG Jiawen; ZOU Shuyun; QIAO Shuaicheng; XIE Wenting

doi:10.19805/j.cnki.jcspe.2025.240280

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Hydraulic Turbine Cavitation State Recognition Method Based on Broad Convolutional Neural Network

更新时间：2026-10-10

- Hydraulic Turbine Cavitation State Recognition Method Based on Broad Convolutional Neural Network
- Chinese Journal of Power Engineering Vol. 45, Issue 7, Pages: 985-991(2025)
- 作者机构：
  
  长沙理工大学能源与动力工程学院,湖南,长沙,410114
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240280
  CLC：
- Online First：14 July 2025，
  
  Published：2025
- 稿件说明：
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刘忠,郑佳稳,邹淑云,乔帅程,谢文婷. 基于宽度卷积神经网络的水轮机空化状态识别方法动力工程学报, 2025, 45(7): 985-991 https://doi.

org/10.19805/j.cnki.jcspe.2025.240280
刘忠,郑佳稳,邹淑云,乔帅程,谢文婷. 基于宽度卷积神经网络的水轮机空化状态识别方法动力工程学报, 2025, 45(7): 985-991 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240280.

org/10.19805/j.cnki.jcspe.2025.240280 DOI：

摘要

针对难以有效提取水轮机声发射(AE)信号故障特征

以及在训练过程中信号频谱故障分类不精确且耗时长的问题

建立了一种宽度卷积神经网络(BCNN)的水轮机空化状态识别方法。采用卷积神经网络(CNN)的卷积层和池化层代替宽度学习(BLS)的线性映射特征层

将得到的CNN特征节点作为增强层的输入

通过训练网络参数建立BCNN模型。利用BCNN模型对不同工况下的水轮机空化AE信号进行分类训练和测试。结果表明:所建立的BCNN模型能对水轮机4种空化状态进行准确快速的分类识别

其平均识别准确率达99.37%

平均耗时为44.16 s。

Abstract

In response to the difficulties in effectively extracting fault characteristics from acoustic emission (AE) signals of the hydraulic turbines

as well as the problems of imprecise and time-consuming fault classification in signal spectrum during the training process

a method for hydraulic turbine cavitation state recognition based on broad convolutional neural network (BCNN) was established. The linear mapping feature layers of broad learning system(BLS) were replaced by the convolutional layers and pooling layers of convolutional neural network (CNN)

the obtained CNN feature nodes were used as inputs for the enhancement layers. By training the network parameters

a BCNN model was built. The BCNN model was used to classify

train and test AE signals from the hydraulic turbine cavitation under different working conditions. Results show that the established BCNN model can accurately and quickly classify and recognize four different cavitation states of the hydraulic turbines

achieving average recognition accuracy rate of 99.37%

with average time consumption of 44.16 s.

关键词

Keywords

references

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