Data-Driven Condition Monitoring of Control Valves 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. In contrast to the state of the art condition monitoring in control valves, the 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.