基于 TS-ED 的半监督化工过程故障诊断方法.

Aiming at the limitation that the existing chemical process fault diagnosis methods based on deep learning usually need complete labeled data to build a fault diagnosis model, this paper proposed a semi-supervised fault diagnosis method for chemical process based on temporal ensembling-dual student model. Firstly, based on the dual student model, the method guided the mutual training through the classification constraint, the stability constraint and the consistency constraint, which effectively alleviated the error accumulation. Then it used temporal ensembling to integrate the prediction of multiple previous network evaluations as consistent regularization objects to alleviate the prediction noise and reduce the training time of the model, so as to improve the classification performance and realize fault diagnosis. Finally, this paper verified the validity and feasibility of the proposed method by the Tennessee-Eastman chemical process benchmark data. Compared with supervised methods such as BNLSTM, DCNN and MCLSTM, it proves that TE-DS algorithm is superior to fault diagnosis. [ABSTRACT FROM AUTHOR]