摘要
在大数据样本条件下,提出一种基于生成对抗网络模型的故障诊断方法。构建生成对抗网络模型,保证模型判别器输出数据的总体分布与原始故障集相近,并基于空间测量工具优化梯度函数,降低模型损失;采用故障集图像转换方式实现对原始故障信号的降维处理,利用判别器的神经网络结构,训练故障数据集,并提取出机械故障集中的故障特征点。实验结果表明,所提方法具有良好的分类诊断性能,故障诊断精度能够达到99.45%。
Under the condition of large fault samples,a fault diagnosis method based on the generation countermeasure network model is proposed.The model of generative adversarial network is constructed to ensure that the overall distribution of output data of model discriminator is similar to the original fault set,and the gradient function is optimized based on the spatial measurement tool;the method of fault set image conversion is used to realize the dimensionality reduction of the original signal,and the neural network structure of discriminator is used to train the input data and extract the fault features of the mechanical fault data set.The experimental results show that the proposed method has good performance of classification and diagnosis,and the accuracy of fault diagnosis can reach 99.45%.
作者
刘伟君
Liu Weijun(Yishui Branch of Linyi Ecological Environment Bureau, Shandong Yishui, 276400, China)
出处
《机械设计与制造工程》
2021年第4期57-62,共6页
Machine Design and Manufacturing Engineering
关键词
大数据样本
半监督
生成对抗网络
梯度函数
分类诊断
big data sample
semi supervision
generative adversarial network
gradient function
classification diagnosis
