Dynamic Inner Canonical Variate Network for Incipient Fault Monitoring

The nonlinear and dynamic nature of complex industrial processes presents a significant challenge for monitoring incipient faults. To this end, this article proposes a novel deep dynamic latent variable model called dynamic inner canonical variate network (DiCVNet). The developed DiCVNet, which is in an end-to-end learning framework, consists of a dual convolutional autoencoder (DuCAE) and an autoregressive (AR) module. First, the DuCAE architecture with an AR module is designed to extract two correlated and self-orthogonal nonlinear dynamic canonical variables (CVs) from past and future datasets for tiny variation modeling. The AR module is embedded to extract the CVs with consistent dynamics for enhanced dynamic modeling of DuCAE. Then, a new incipient fault monitoring scheme for nonlinear dynamic processes is established. Finally, the performance of the proposed method is verified by two industrial cases, that are, a continuous stirred tank reactor and a multiphase flow process.