![]() ![]() Here, during the SEAE training process, each layer of the enhanced autoencoder (EAE) network reconstructs both the network input and the original input. Secondly, based on the reconstructed process data, the SEAE network is used to deeply extract data feature information and achieve high accuracy regression prediction. Firstly, the raw industrial process data are decomposed and reconstructed using VMD to achieve denoising and reduce the non-smooth characteristics of the data series. Considering the nonlinearity, time-varying, and repetitive nature of the batch process, this paper proposes a soft sensor modeling method (VMD-SEAE-TL) based on variational mode decomposition (VMD), stacked enhanced autoencoder (SEAE) and transfer learning (TL) algorithms for online detection of key variables in batch industrial production processes. On complex batch industrial processes, soft sensor modeling plays a key role in process control and monitoring. The experimental results show that the proposed method has accurate prediction performance. īased on two actual industrial process cases, the proposed soft-sensor modeling method is used for the online prediction of quality-related variables.Also, the proposed method retains the source domain data feature information, which enables the transferred model to accurately predict the key variables in the target domain, thus solving the domain adaptation problem as well. The method is integrated into VMD-SEAE for online fine-tuning of SEAE network models to avoid model retraining. ![]()
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