基于卷积神经网络的蒸汽动力系统故障诊断

Fault diagnosis of steam power system based on convolutional neural network

    摘要
  • 摘要:
      目的  为了提高船用动力系统的故障诊断水平，基于卷积神经网络对船用增压锅炉进行实时诊断研究。
      方法  首先，基于GSE平台开发船用增压锅炉的仿真程序，获得模拟故障数据，在此基础上利用卷积神经网络方法建立增压锅炉的故障诊断模型；然后，根据温度、流量等参数的变化趋势，结合先验知识与机器学习方法进行故障识别，并采用混淆矩阵、精确度等评价标准对该方法进行性能评估。
      结果  根据特征提取后的数据集与原始数据集的对比结果，模型输出结果的稳定性与模型的泛化能力均得以优化提升，整体故障分类精度可达99.53%。
      结论  研究成果可为船用动力系统的智能化监测提供参考。

     

    Abstract:
      Objectives  In order to improve the fault diagnosis level of marine power systems, this paper studies the real-time fault diagnosis of a marine supercharged boiler based on a convolutional neural network (CNN).
      Methods  First, the simulation program of the marine supercharged boiler is developed based on the GSE platform, and the simulation fault data is obtained. The fault diagnosis model of the boiler is then established using the CNN method. Next, through the change trends of temperature, flow and other parameters, combined with a priori knowledge and the machine learning method, fault identification is carried out. Lastly, the performance of the method is evaluated against criteria such as confusion matrix and accuracy.
      Results  According to the comparison results between the feature extracted dataset and the original dataset, the stability of the model output results and the generalization ability of the model are optimized and improved, with an overall fault classification accuracy reaching 99.53%.
      Conclusion  The results of this study can provide valuable references for the intelligent monitoring of marine power systems.

     

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