A dynamic-inner convolutional autoencoder for process monitoring

Modern manufacturing industries are urgently demanding intelligent process monitoring systems for plant maintenance and accident prevention in the Industry 4.0 era. With the rapid development of deep learning, data-driven process monitoring methods are attracting wide attention and have been applied to many processes. However, most deep learning methods do not model process latent dynamics and are deficient to detect dynamic variations. In this work, a novel dynamic-inner convolutional autoencoder (DiCAE) is proposed. Unlike previous autoencoders that only focus on input reconstruction, DiCAE innovatively integrates a vector autoregressive model into a 1-dimensional convolutional autoencoder to monitor nonlinear processes, as well as capture process dynamics. When applied to a numerical simulation, DiCAE could detect the dynamic variation and distinguish different process data into separate clusters with an intuitive visualization, while other conventional methods cannot. The effectiveness of DiCAE is also demonstrated on the benchmark Tennessee Eastman process. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

In the Industry 4.0 era, intelligent process monitoring systems are in high demand for plant maintenance and accident prevention. This study proposes a novel dynamic-inner convolutional autoencoder (DiCAE) that incorporates a vector autoregressive model to monitor nonlinear processes and capture process dynamics. Numerical simulations and benchmark tests demonstrate the effectiveness of DiCAE in detecting dynamic variations and distinguishing different process data.