Variational autoencoder based on distributional semantic embedding and cross-modal reconstruction for generalized zero-shot fault diagnosis of industrial processes

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. To solve this problem, a generalized zero-shot fault diagnosis model for industrial processes based on distributional semantic embedding and cross-modal reconstruction variational autoencoder (DSECMR-VAE) is proposed. DSECMR-VAE uses two variational autocoders (VAEs) to encode the features from two different modalities of fault samples and fault attribute semantic vectors into latent variables, and then uses the latent variables to generate fault samples belonging to the seen and unseen fault classes. In addition, a Barlow matrix is designed specifically for the distribution parameters of the latent variables, this matrix is utilized to measure the consistency between the distribution of fault samples and fault attribute semantic vectors. Subsequently, cross-modal reconstruction is performed on VAE. Cross-modal reconstruction uses the input from different modalities to reconstruct the current input, which can fully combine the information from different modalities. Finally, a classifier is trained based on the obtained latent variables to realize the generalized zero-shot fault diagnosis. Two case studies demonstrate the effectiveness and superiority of the proposed model for zero-shot and generalized zero-shot fault diagnosis.