Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processes
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929303233495040 |
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| author | Frey, Christian W. |
| author_facet | Frey, Christian W. |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processes Frey, Christian W. Machine Learning Systems and Control Signal Processing 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 |
| title | Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processes |
| topic | Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2403.13032 |