Industrial Process Monitoring Based on Deep Gaussian and Non-Gaussian Information Fusion Framework

Abstract

For industrial process monitoring, Gaussian and non-Gaussian data-driven models are two important representatives that have been developed separately in the past years. Although several attempts have been made to combine Gaussian and non-Gaussian data information for integrated process monitoring, this information fusion strategy can be further enhanced under the idea and framework of deep learning. Particularly, through collaborative learning and layer-by-layer information transformation, more patterns of both Gaussian and non-Gaussian components can be effectively extracted in different hidden layers of the deep model. Then, a further Bayesian model fusion strategy is formulated to ensemble monitoring results from both Gaussian and non-Gaussian data-driven models. Therefore, the main contribution of this article is to propose a deep Gaussian and non-Gaussian information fusion framework for data-driven industrial process monitoring. Both feasibility and superiority of the developed model are confirmed through a detailed industrial benchmark case study. Compared to both Gaussian and non-Gaussian deep models, the new deep information fusion model has obtained more satisfactory monitoring results.