Developing semi-supervised latent dynamic variational autoencoders to enhance prediction performance of product quality. (16th January 2023) - Lee, Yi Shan; Chen, Junghui
Highlights: Dynamic features of process and quality data are learned for quality prediction. Bi-directional RNN is trained by past and future data to prevent over-fitting. The unlabeled process data are used to enhance the quality prediction performance. Improved loss functions for different data types enables parallel parameter update. Abstract: The online quality variables of soft sensors contribute greatly to obtaining immediate process information. The complex correlations between a large number of process variables inherited from the dynamic and nonlinear characteristics of chemical processes put more challenges on constructing soft-sensor models. Past developed steady-state soft sensors are not reliable for dynamic operating systems. Unequal sampling rates for the process and quality data cause missing values of quality data at some time points. This paper proposes a semi-supervised latent dynamic variational autoencoder to learn features between the process and quality data. A prediction network is constructed to generate artificial quality values for model training. Then the process and quality data are compressed into the latent space and the temporal relation is modeled in the clean latent space. The proposed method is compared with the conventional method for quality prediction in a numerical case and an industrial case.