A generalized zero-shot semantic learning model for batch process fault diagnosis

Abstract In industrial monitoring, although zero-shot learning successfully solves the problem of diagnosing unseen faults, it is difficult to diagnose both unseen and seen faults. Motivated by this, we propose a generalized zero-shot semantic learning fault diagnosis model for batch processes called joint low-rank manifold distributional semantic embedding and multimodal variational autoencoder (mVAE). Firstly, joint low-rank representation and manifold learning makes the training samples map to the low-rank space, which obtains the global–local features of the samples while reducing the redundancy in the inputs for the training model; secondly, the bias of human-defined semantic attributes is corrected by predicting the attribute error rate; then, fault samples and corrected semantic vectors are embedded into the consistency space, in which the samples are reconstructed using the mVAE to fully integrate the cross-modal information, meanwhile, Barlow matrix is designed to measure the consistency between the fault samples and the attribute vectors, the higher the consistency, the higher the learning efficiency of attribute classifiers; finally, the generalized zero-shot fault diagnosis experiments are designed and conducted on the penicillin fermentation process and the semiconductor etching process to validate the effectiveness, the results show that the proposed model is indeed possible to diagnose target faults without their samples.