Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processes

arXiv:2403.13032v1 Announce Type: new
Abstract: Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative …

abstract arxiv complex systems continuous continuous monitoring cs.lg cs.sy eess.sp eess.sy efficiency hybrid industrial industry limitations maps monitoring paper pharmaceutical pharmaceutical industry processes product production production processes quality safety strategy systems type unsupervised unsupervised learning