Novel Feature-Disentangled Autoencoder Integrating Residual Network for Industrial Soft Sensor

Abstract

In order to overcome the low robustness and weak generalization in existing deep autoencoder (AE) for soft sensor modeling, a novel feature-disentangled autoencoder (FDAE) integrating residual network (Resnet) (FDAE-Resnet) is proposed in this paper. Different from the traditional deep AE that only can learn entangled features, the FDAE can obtain disentangled multi-source features including trend features, periodic features and spatial features by a new trend-periodic long short-term memory (TPLSTM) and a novel dynamic self-attention convolutional neural network (DSACNN). The trend and periodic signals decomposed from input variables are fed into the TPLSTM to learn trend and periodic features in time and frequency domain, respectively. Then, the DSACNN is utilized to capture dynamic spatial features in spatial domain by adding a new attention mechanism. Moreover, disentangled multi-source features are obtained by concatenating trend features, periodic features and spatial features together. Finally, the Resnet is utilized to build the soft sensor model by establishing the relationship between disentangled multi-sources features and outputs. To illustrate the effectiveness and superiority of the proposed method, the FDAE-Resnet is applied in the actual polypropylene process industry for melt index modeling. The experiment results show that compared with other state-of-the-art methods, the FDAE-Resnet can reduce the root mean square error by 26.2% and the mean absolute percentage error by 38.2% on average in the changed working conditions, respectively.