LAUSR

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 abovementioned three problems simultaneously, this article 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 the block diagonal kernel function matrix and spectral clustering. For the online sample, a mode indicator is derived from the 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.