Monitoring of Nonlinear Processes With Multiple Operating Modes Through a Novel Gaussian Mixture Variational Autoencoder Model
Peng Tang, Kaixiang Peng, Jie Dong, Kai Zhang, Shanshan Zhao
Abstract
Customized production, quality variation of raw materials and other factors make industrial processes work in multiple operating modes. In general, complex industrial processes have strong nonlinearity under each operating mode. In this paper, a Gaussian mixture variational autoencoder (GMVAE) model, which combines with Gaussian mixture and VAE, is proposed to monitor nonlinear processes with multiple operating modes. Due to the Gaussian mixture distribution limitation in latent variable space, GMVAE can not only automatically extract features of the nonlinear system, but also make these features follow Gaussian mixture distribution. Based on Gaussian mixture distribution in latent variable space and the reconstruction error, two probability monitoring indexes are constructed, whose control limits can be determined by χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution. TE benchmark data and real hot strip mill process (HSMP) data have been used to verify the effectiveness of the proposed method.