A Novel Distributed Fault Detection Approach Based on the Variational Autoencoder Model

Abstract

In large-scale industrial fault detection, a distributed model is typically established on the basis of blocked units. However, blocked distributed methods consider units as independent of one another and disregard the relationship between units, thus leading to incomplete information on local units. In fact, the operation status of a unit is affected by a local unit and its surrounding neighboring units. In addition, the fault detection performance of a system is seriously reduced once data are missing from the data source. Variational autoencoder (VAE) is not only a popular deep generative model but also has a powerful nonlinear feature extraction capability. In this study, VAE is extended to the distributed case. In this study, a distributed fault detection method DVAE based on VAE is proposed. This method can not only describe local and neighboring information, but it can also reconstruct missing data. First, system variables are divided into local and neighboring units in accordance with the system mechanism. Second, for each local unit, a DVAE model is established to map the multivariable data onto the latent variable space. The obtained latent variable contains the information on a local unit and can reflect the complex relationship with its neighboring units. Lastly, Euclidean distance is used to detect system faults. When applied on the Tennessee Eastman process for verification, the proposed method shows good performance in fault detection and missing data reconstruction.