Operating performance assessment of industrial process based on MIC-graph convolutional networks with local slow feature analysis.

To ensure the safe and stable operation of industrial processes, deep neural network-based operational performance assessment methods have been extensively adopted according to the latest research findings. However, existing industrial process performance assessment models often fail to account for the local spatial structure features and the slowly varying features from time series samples. Such limitations result in the suboptimal exploitation of spatial interaction information and hinder the models' responsiveness to complex system state transitions, thereby impeding the precise assessment of industrial process performance. To this end, a maximum information coefficient-based graph convolutional networks (MIC-GCN) is proposed for operational performance assessment, which aims to effectively capture the intricate interactions of latent spatial structures embedded in temporal process data. First, a MIC-based graph construction method is employed to transform time series data into graph-structured data with nodes and edges, thereby preserving the local geometric structure of the original data and revealing high-dimensional spatial interaction information among data samples. Second, local slow feature analysis (SFA) is utilized to extract fine-grained dynamic correlation information from the spatial structure of the data. Furthermore, the Siamese GCNs are designed to simultaneously process graph-structured data samples at two consecutive time steps, which facilitates the capture of slowly varying feature representations embedded in the evolving topological structures. The proposed method can precisely extract and deeply mine spatiotemporal interactive features information, thereby enhancing the accuracy of performance assessment. Experimental validation on coal slurry flotation and dense medium coal preparation platforms confirms the method's efficacy and reliability.