Evaluation of neural network models and quality forecasting based on process time-series data

Complex industrial process modelling is critically important within the context of industrial intelligence. In recent years, soft sensor techniques based on neural networks have become increasingly popular for modelling nonlinear industrial processes. This paper proposes an integrated framework of neural network modelling and evaluation for nonlinear dynamic processes. This framework achieves an integrated solution for modelling, prediction, evaluation, and network structure parameter selection. It can be applied to noisy sensors and dense data in the time domain. The framework's proposed evaluation mechanism employs two novel evaluation metrics, the variational auto-encoder (VAE)-based Kullback-Leibler (KL) divergence metric and the maximum likelihood estimation-based J metric, which both evaluate the model by mining the statistical properties of the residuals. The framework models the dynamic process with a model order based-gated recurrent units (MOb-GRU) neural network and a modified transformer model. Numerical experiments demonstrate that the evaluation mechanism functions properly in scenarios with multiple signal-to-noise ratios and multiple noise statistical properties and that the framework produces accurate modelling results.

Automated summary

This paper proposes an integrated framework of neural network modelling and evaluation for nonlinear dynamic processes. The framework can handle noisy sensors and dense data, and employs two novel evaluation metrics to evaluate the model. Numerical experiments demonstrate the accuracy and stability of the framework.