Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processes
This work addresses monitoring challenges in industrial processes like pharmaceuticals, but it appears incremental as it combines existing techniques.
The paper tackled monitoring complex industrial processes by proposing a hybrid unsupervised learning strategy (HULS) to address limitations of Self-Organizing Maps, such as unbalanced data and correlated variables, and performed comparative experiments to evaluate its performance.
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