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Quality-relevant fault detection plays an important role in industrialprocesses, while the current quality-related fault detection methods based onneural networks main concentrate on process-relevant variables and ignorequality-relevant variables, which restrict the application of processmonitoring. Therefore, in this paper, a fault detection scheme based on theimproved teacher-student network is proposed for quality-relevant faultdetection. In the traditional teacher-student network, as the featuresdifferences between the teacher network and the student network will causeperformance degradation on the student network, representation evaluation block(REB) is proposed to quantify the features differences between the teacher andthe student networks, and uncertainty modeling is used to add this differencein modeling process, which are beneficial to reduce the features differencesand improve the performance of the student network. Accordingly, REB anduncertainty modeling is applied in the teacher-student network named asuncertainty modeling teacher-student uncertainty autoencoder (TSUAE). Then, theproposed TSUAE is applied to process monitoring, which can effectively detectfaults in the process-relevant subspace and quality-relevant subspacesimultaneously. The proposed TSUAE-based fault detection method is verified intwo simulation experiments illustrating that it has satisfactory faultdetection performance compared to other fault detection methods.

Representation Evaluation Block-based Teacher-Student Network for the Industrial Quality-relevant Performance Modeling and Monitoring