摘要 : Recently, latent models have continued to find advantages in statistical process monitoring, especially with multifarious extensions coping with the nonlinearity and dynamics confronted by the pattern extraction. However, when lat... 展开 Recently, latent models have continued to find advantages in statistical process monitoring, especially with multifarious extensions coping with the nonlinearity and dynamics confronted by the pattern extraction. However, when later performing the famous T~2 statistic on the patterns, the issue of non-Gaussianity has rarely been focused alongside, hence vitiating the control of Type-I and -II error in practice. In this paper, a model, in the name of recurrent Kalman variational auto-encoder (RKVAE), is theoretically derived for handling these properties simultaneously, and proposed for its use on monitoring general complex processes. The main idea is to build a monitoring panel on the basis of further reasoning the dynamics underlying the nonlinearly extracted Gaussian features. The model hierarchically consists of three parts: the vanilla VAE for encoding latent Gaussian features, a linear Gaussian state-space model (LGSSM) for temporally reasoning these features, and an RNN for recurrently updating the online LGSSM by fusing several candidate parameters. For the latter K in the name, it is the Kalman filter (KF) that facilitates the feature noise filtering and LGSSM identification. The model has, structurally, a good capacity to be extensible from both the depth and width when modelling complex processes. Finally, based on the explicit formula of LGSSM, a detection panel is designed, and two case studies are given to demonstrate its effectiveness in monitoring and relevant analysis. 收起