Data-driven modeling and analysis based on complex network for multimode recognition of industrial processes

Abstract

Abstract An industrial process usually has multiple operating conditions or periods due to various factors such as the fluctuation of raw material quality, differences of worker levels, and changes of product specifications. Industrial data from different operating conditions or periods have different statistical characteristics, namely multimode behavior. In fact, multimode recognition of industrial processes is the first problem to develop effective intelligent manufacturing strategies for engineers. The clustering-based methods can help them characterize multimode behavior in historical databases and group them together with little detailed knowledge of the process. However, these methods are facing the challenges of massive high-dimensional industrial big data and unknown mode number. This study proposes a construction method of complex network to describe the potential relationship in industrial big data. The topological structure of complex network can be acquired by taking the correlation matrix of multi-variables as nodes and the similarity between correlation matrices as edges. On this basis, community detection technology is employed to reveal the aggregation behavior of nodes in network. It takes the maximum modularity as the optimization objective, and partitions each node into one community through greedy search algorithm, where a community denotes a mode of industrial process. Finally, case studies have demonstrated the proposed method has good performance and application prospects.