Name
Abstract | ||
|---|---|---|
Classification with multivariate signal data is an important machine learning task in artificial intelligence applications in the process industry. Examples of such applications range from process monitoring and optimization, product quality prediction, or predictive maintenance. Signal data captures physical quantities like pressures, flows, levels, temperatures, vibrations, etc. Although relative large historical data sets are available in process plants, a common problem in the development of classification models - especially for process monitoring and optimization - is the lack of labels. We introduce a first version of an active learning web-application that can support human experts in providing labels for the identification of steps in batch recipe from process data while gaining insights into the learning progress of the machine learning model. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ETFA52439.2022.9921701 | 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) |
Keywords | DocType | ISBN |
active learning application,chemical batch production,multivariate signal data,important machine learning task,artificial intelligence applications,process industry,product quality prediction,predictive maintenance,signal data captures physical quantities,relative large historical data sets,process plants,classification models,process monitoring,optimization,active learning web-application,batch recipe,process data,learning progress,machine learning model | Conference | 978-1-6654-9997-2 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asif Ahmad | 1 | 0 | 0.34 |
| Chen Song | 2 | 0 | 0.34 |
| Ruomu Tan | 3 | 0 | 0.34 |
| Marco Gärtler | 4 | 0 | 0.34 |
| Benjamin Klöpper | 5 | 0 | 0.34 |