基于深度学习的船用核动力管路系统故障诊断方法研究

针对船载核动力反应堆管路特征参数选取严重依赖人工经验和诊断准确率低的问题,本文引入机器学习的思想,提出了一种基于卷积神经网络的船载核动力反应堆管路故障诊断方法,以提高船载核动力反应堆管路故障诊断的智能化水平。首先使用卷积神经网络建立分类模型,并利用该模型对22类数据进行分类性能测试;然后提取反映管路运行状态的特征参数,输入深度学习分类器中进行诊断。使用现有管路故障诊断数据验证了本方法的实用性和有效性。

In view of the Characteristic parameters for ship-borne nuclear reactor line selection relies heavily on the artificial experience and diagnose the problem of low accuracy,introducing the idea of machine learning,this paper proposes a ship nuclear power reactors based on convolution neural network line fault diagnosis methods,in order to improve the ship nuclear reactor line fault diagnosis of intelligent level.Firstly,a classification model is established by using conversational neural network,and the classification performance of 22 kinds of data is tested by this model.Then the characteristic parameters reflecting the running state of the pipeline are extracted and input into the deep learning classifier for diagnosis.The practicability and effectiveness of this method are verified by using the existing pipeline fault diagnosis data.