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.

[2021-11-29] Accepted Paper /doc/2021.08.0362/v1

[2022-03-05] Printed Article /doc/2021.08.0362/v2