Joint structure bipartite graph projection and its application for industrial process monitoring

Abstract

Process monitoring plays a crucial role in maintaining the safety and stable operation of industrial processes. One of its key tasks is to improve the accuracy of detecting abnormal conditions. To this end, we propose a dimensionality reduction method called Joint Structure Bipartite Graph Projection. This method not only captures global information, but also incorporates manifold learning and anchor strategy to capture the local structure of data in an adaptive neighbor manner. In constructing the bipartite graph between projected samples and projected anchors, structural information between samples and anchors is fully considered to ensure an effective graph. Furthermore, we introduce an information entropy-based method to compute the balance parameters, reducing costs of parameter tuning. Ultimately, we develop a process monitoring model based on JSBGP method, achieving the goals of anomaly detection and abnormal variables isolation. Through experiments on benchmark dataset and actual industrial process demonstrate the effectiveness of the proposed method.