Density peaks clustering based on multiple distance measures for manufacturing process

Data analysis and processing of manufacturing process is significant to ensure the stable production safety, maintain quality stabilization, and optimize production profit. Practical manufacturing process often has complex characteristics, such as multimode, nonlinearity, etc. Mode division can divide manufacturing process into multiple modes and is useful for subsequent process monitoring and scheduling optimizing. In this paper, density peaks clustering (DPC) based on multiple distance measures is used for mode division in manufacturing process. Multiple distance measures for computing the local density and minimum distance between the point and any other point with higher density in DPC are compared and analyzed. To illustrate the effectiveness of the clustering method for mode division in manufacturing process, experiments are developed based on penicillin fermentation process and practical foods industrial production process. Experimental results verify the feasibility and efficiency of the clustering method for mode division in manufacturing process.