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 experiments are performed based on a laboratory batch

翻译：工业生产过程，尤其是制药行业中的复杂系统，需要持续监测以确保效率、产品质量和安全性。本文提出了一种混合无监督学习策略（HULS），用于监测复杂工业过程。针对传统自组织映射（SOMs）的局限性，特别是在数据集不平衡和过程变量高度相关的场景中，HULS结合现有的无监督学习技术来应对这些挑战。为评估HULS概念的性能，基于实验室批次进行了对比实验。