基于CNN-RF的嵌入式数控系统故障诊断研究

采用Stacking集成策略,融合卷积神经网络(CNN)和随机森林(RF)法提出一种故障诊断方法CNN-RF。该方法不仅能准确提取数据集中的数据特征,而且针对数据集中故障数据数量不足的问题能提供平衡数据集误差的有效方法,以提高诊断的准确性。分别采用单独模型和集成后的模型对采集到的嵌入式数控系统实时运行数据进行分析处理。结果表明:利用CNN-RF模型进行嵌入式数控系统故障诊断的准确度较高,验证了该模型的正确性。

Research on Fault Diagnosis of Embedded CNC System Based on CNN-RF

By using Stacking ensemble strategy, a fault diagnosis method CNN-RF was proposed by integrating convolutional neural network (CNN) and random forest (RF) method. By using this method, not only the data features in the data set could be accurately extracted, but also an effective method could be provided to balance the errors of the data set for the problem of insufficient fault data in the data set, so the diagnostic accuracy could be improved. The collected real-time running data of embedded CNC system were analyzed and processed by using the separate model and the integrated model.The results show that by using the CNN-RF model, the accuracy of the fault diagnosis is high, by which the correctness of the model is verified.