A variational autoencoders approach for process monitoring and fault diagnosis. (14th September 2021) - Tang, Peng; Peng, Kaixiang; Dong, Jie; Zhang, Kai; Jiao, Ruihua
Probabilistic models, which can model the process noise and can handle the problem of missing data in the probabilistic framework, recently have been got much attention in process monitoring and fault diagnosis area. This paper presents a new probabilistic methodology for fault detection and diagnosis in nonlinear processes using a variational autoencoders (VAEs) models. Two statistic index, based on the probability density distribution of measure variables and latent structure variable, are built to monitoring fault. Then a probabilistic contribution analysis method, based on the concept of missing variable estimation, is proposed for fault diagnosis. The performance of fault detection and diagnosis is demonstrated through its application for the monitoring of Tennessee Eastman (TE) industrial process, and the effectiveness is verified.