基于网络结构搜索的工业过程自动故障诊断方法

针对现有基于深度神经网络的工业过程故障诊断方法存在网络结构设计烦琐及参数寻优耗时等问题,提出了一种基于网络结构搜索的工业过程自动故障诊断方法(automatic fault diagnosis, AutoFD),该方法采用AutoFD网络结构搜索算法,来自动完成卷积神经网络的网络结构设计和网络参数寻优。在此基础上,首先通过在原始数据上施加操作生成新通道;接着利用表现预测加速获取通道适应性排序的过程;然后依据通道适应性排序,通过表现预测来快速选取最优卷积通道数;最终根据最优卷积通道来搜索表现最优的多通道卷积神经网络模型用于工业过程自动故障诊断。采用田纳西—伊斯曼(Tennessee Eastman, TE)工业过程和数值系统对提出方法进行验证,结果表明该方法可以实现网络结构自动设计及网络参数的自动寻优,并且具有优良的故障诊断性能。

Automatic fault diagnosis method based on neural architecture search for industrial processes

In the industrial process, the existing fault diagnosis methods based on deep neural network are a challenging pro-blem due to complicated network structure design and time-consuming parameter optimization. To achieve the problem, this paper proposed an automatic fault diagnosis method(AutoFD) based on neural architecture search. The method used the AutoFD neural architecture search(NAS) algorithm to automatically complete the network structure design of the convolutional neural network and the optimization of the network parameters. On this basis, some new channels were firstly generated by operating the original data. Performance prediction was then used to speed up the process of acquiring channel adaptive sorting, and the optimal number of convolutional channels was quickly selected according to the adaptive sorting of the channels. Finally, through the optimal convolution channels, the best-performing multi-channel convolutional neural network model for automatic fault diagnosis in chemical processes could be searched. It was applied to the Tennessee Eastman(TE) industrial process and numerical system for fault diagnosis to verify the proposed method. The results show that the AutoFD method can automatically design the network structure and optimize the parameters of the multi-channel convolutional neural network, which has excellent performance in fault diagnosis.