Data-driven approach to support experts in the identification of operational states in industrial process plants

Knowledge on the operating conditions of chemical processes is important for various applications, for instance failure diagnosis. In absence of the recorded information, it has to be reconstructed from other data. This is commonly carried out by process experts as the identification of the operating states requires a deep understanding of the underlying process. As manual classification is a time-consuming task, an automatic identification can simplify this task greatly. As data-driven approaches often fail due to the complex nature of the processes, two hybrid approaches are proposed in this paper, supporting the expert during the classification. Big data methods are used for processing with experts having the chance to influence and evaluate the algorithms and results. For identification, a k-Means clustering and a combination of self-organizing maps and hidden Markov models are used. While minimizing the expert effort for classification, the results still have to be reliable. Both approaches performed accurate and decreased the expert effort significantly. Future studies are centered on combining both methods as their strengths complement each other.