Keusch, AlexanderAlexanderKeuschHiessl, ThomasThomasHiesslJoksch, MartinMartinJokschSundermann, AxelAxelSundermannSchall, DanielDanielSchallSchulte, StefanStefanSchulte2023-09-252023-09-252023-0721st IEEE International Conference on Industrial Informatics (INDIN 2023)9781665493130https://hdl.handle.net/11420/43462Monitoring 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.batch-based processesedge intelligenceindustrial IoTprocess monitoringComputer SciencesConference Paper10.1109/INDIN51400.2023.10217845Conference Paper