Automatic multimode identification of complex industrial processes based on network community detection with manifold similarity

Abstract

Complex industrial processes usually exhibit multimode characteristics, meaning that statistical features of process data, such as mean, variance, and correlation, vary across different modes. Extracting critical information from these distinct modes can significantly enhance the accuracy and robustness of data-driven models in process monitoring, condition evaluation, and quality improvement. Consequently, the multimode identification of industrial data becomes a paramount concern in data-driven modelling. However, existing methods for multimode identification require prior knowledge to predetermine the number of modes and struggle to describe the similarity between high-dimensional samples effectively. To address this issue, this study introduces an automatic multimode identification method based on complex network community detection. In this approach, each data sample is considered as a node, and manifold similarity is calculated to construct the complex network model. The method leverages weighted geodesic distances to capture the data's manifold structure and potential density, enabling better distinction between high-dimensional samples in different modes. The greedy search algorithm with modularity maximisation is employed to partition nodes into modes without manual selection of the number of modes. Furthermore, a node degree-based indicator is developed for online mode monitoring. Experimental studies on two examples demonstrate the effectiveness of the proposed method in uncovering multimode characteristics of complex industrial processes, highlighting its promising application potential.