Edge Intelligence for Detecting Deviations in Batch-based Industrial Processes

Citation
21st IEEE International Conference on Industrial Informatics (INDIN 2023)
Contribution to Conference
21st IEEE International Conference on Industrial Informatics, INDIN 2023
Publisher DOI
10.1109/INDIN51400.2023.10217845
Scopus ID
2-s2.0-85171173250
ISSN
19354576
ISBN
9781665493130
Monitoring of batch production processes is complex and existing solutions do not offer good performance in providing real-time feedback about the state of the process. Therefore, we introduce an AI system that monitors a fermentation process and detects deviations from the normal process execution directly on the edge and provides real-time feedback to the operator, allowing intervention before the process gets out of control. We analyze the accuracy of the novel AI-based approach by carrying out several experiments and compare the outcome with statistical methods as a baseline. The experiments show that the AI-based approach performs significantly better at detecting anomalies in a fermentation process than the statistical methods.
Subjects
batch-based processes
edge intelligence
industrial IoT
process monitoring
DDC Class
004: Computer Sciences