Active Learning Application for Recognizing Steps in Chemical Batch Production

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.