Robust Decomposition of Kernel Function-Based Nonlinear Robust Multimode Process Monitoring

Abstract

With the rapid development of modern industry, actual production processes generally have a variety of complex characteristics including nonlinearity, multimodality, and contamination. Those characteristics, as well as the faults, bring great challenges to traditional process monitoring. To deal with all the above-mentioned three problems simultaneously, this paper develops a robust nonlinear multimode process monitoring scheme. First, the robust decomposition of kernel function (RDKF) algorithm is proposed to detect outliers. Then, a nonlinear mode identification method is presented by combining block diagonal kernel function matrix and spectral clustering. For the online sample, a mode indicator is derived from kernel function to judge whether it belongs to a fault or a certain mode. Finally, the effectiveness of the proposed method is validated by two cases in terms of both mode identification and fault detection.