Data-Driven Modelling of Control Valve Behaviour in Laboratory Test Runs

The availability of huge amounts of process data enables data-driven methods to optimize production processes. Predictive Maintenance is one of the common applications to transfer data to useful information for improving the Overall Equipment Effectiveness. In this paper, a data-driven method for condition monitoring of control valves in industrial process plants is developed based on data collected during test runs. These test runs make it possible to define a threshold that differentiates normal from abnormal valve behaviour. Furthermore, the characteristics of the model results allow the identification of different defects. The application of the proposed method to a historic industrial data set validate the applicability in noisy industrial use cases.