Augmenting deviation of faults from the normal using fault assistant Gaussian mixture prior variational autoencoder

Abstract

In this new era of Industry 4.0, manufacturers tend to store process data from the entire production, regardless of whether they are “normal” or “faulty” for further data analysis. However, almost all the existing monitoring models are constructed based on normal data instead of abnormal data. In fact, the “normal” and some of the “faulty” data originate from the same production line. Thus, not only the normal data but also the abnormal ones can be used to improve the monitoring performance of conventional monitoring performance by simultaneously sharing and extracting common knowledge. In this paper, a fault assistant Gaussian mixture prior variational autoencoder (FA-GMPVAE) is proposed to perform information sharing and enhance the statistic model for the normal operating region. Unlike an ordinary variational autoencoder (VAE) and an ordinary Gaussian mixture prior variational autoencoder (GMPVAE), the structure of FA-GMPVAE is a combination of a “normal” based VAE network and a “normal-relevant” based GMPVAE (NR-GMPVAE) network. FA-GMPVAE can make the shared information non-negative to prevent information loss because only the normal-relevant common information is shared by the one-step transfer learning procedure. In addition, fault diagnosis of NR-GMPVAE can be flexibly updated with the new type of fault. Correspondingly, the probability density estimates of latent variables and residuals instead of point estimates are then given so that distribution-based monitoring indices of the normal data can be designed and the fault detection decisions can be made opportunely. To show the effectiveness of the proposed method, a numerical and a real industrial example are presented.