  Quality-relevant fault detection plays an important role in industrial
processes, while the current quality-related fault detection methods based on
neural networks main concentrate on process-relevant variables and ignore
quality-relevant variables, which restrict the application of process
monitoring. Therefore, in this paper, a fault detection scheme based on the
improved teacher-student network is proposed for quality-relevant fault
detection. In the traditional teacher-student network, as the features
differences between the teacher network and the student network will cause
performance degradation on the student network, representation evaluation block
(REB) is proposed to quantify the features differences between the teacher and
the student networks, and uncertainty modeling is used to add this difference
in modeling process, which are beneficial to reduce the features differences
and improve the performance of the student network. Accordingly, REB and
uncertainty modeling is applied in the teacher-student network named as
uncertainty modeling teacher-student uncertainty autoencoder (TSUAE). Then, the
proposed TSUAE is applied to process monitoring, which can effectively detect
faults in the process-relevant subspace and quality-relevant subspace
simultaneously. The proposed TSUAE-based fault detection method is verified in
two simulation experiments illustrating that it has satisfactory fault
detection performance compared to other fault detection methods.
