UC Davis

Although deep autoencoders excel at extracting intricate features, their application in process monitoring is limited by the requirement for large sample sizes and interpretability of latent representations. This work presents a special deep learning structure named Siamese network to detect abnormal deviations in nonlinear dynamic processes. By leveraging the capability of Siamese architecture to process multiple inputs simultaneously, the training sample size expands exponentially, which enhances the learning potential of the model. Furthermore, a long short-term memory unit is integrated to enable the capture of long-term process dynamics. To refine the distribution of latent features extracted from diverse data types, a contrastive loss function is proposed, which strengthens the model's fault detection capabilities and enhances its interpretation of latent representations. Then T2 statistic is established on the latent space to perform fault detection. The effectiveness of the method is demonstrated through case studies on simulation processes and an industrial process.