Soft sensors are essential for advanced monitoring and control to prevent undesirable operations and improve product quality. However, nonlinear, autocorrelated, and cross-correlated behaviors in industrial data demand concurrent modeling of the dynamics and nonlinearities. Deep learning-based soft sensors, such as recurrent neural network (RNN) and long short-term memory (LSTM) networks, often incorporate complex structures and numerous parameters, which can lead to an overly complex model. In practical applications where training data samples are limited, a lightweight neural network with strong generalization capability is preferred. With a simple structure of feed-forward layers of 1-D convolutional neural networks (CNNs) (1-D-CNN) for time-series data modeling, this article proposes a novel lightweight dynamic CNN (LDCNN) for soft sensors. Positional embedding (PE) and simplified temporal attention mechanisms are integrated for improved dynamic modeling, while dilated convolutions and layer normalization (LN) are incorporated to significantly reduce the depth and width of the network and avoid over-parametrization. Experimental results on a real industrial case indicate that a lightweight model outperforms the traditional methods with limited training samples.
Liu et al. (Thu,) studied this question.