Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis

Abstract

In chemical plants and other industrial facilities, the rapid and accurate detection of the root causes of process faults is essential for the prevention of unknown accidents. This study focused on deep learning while considering the different phenomena that can occur in industrial facilities. A deep convolutional neural network with deconvolution and a deep autoencoder (DDD) is proposed. DDD assesses the process dynamics and the nonlinearity between process variables. During the operation of DDD, fault detection is carried out using the reconstruction error between the data reconstructed through the model and the input data. After a process fault is detected, the magnitude of the contribution of each process variable to the detected process fault is calculated by applying gradient-weighted class activation mapping to the established network. The effectiveness of DDD in fault detection and diagnosis was verified through experiments on the Tennessee Eastman process dataset, demonstrating that it can achieve improved performance compared to the conventional fault detection and diagnosis.