Reliability analysis of Pneumatic Control Valve in Process Plant Using Simulated Failure Data Performance monitoring is an important part of process plants. As it is very well known that process plants consist of many control loops to operate. Although, single downtime in a process plant can cause huge loses in production and financially. Hence it is required to understand the behavior of controllers, such as control valves to avoid the downtime and to reflect the reliability analysis. Valve stiction is the most common and reoccurring problem in pneumatic control valves. Since many existing models define the valve stiction and how to control stiction effect, but how the stiction is going to affect the reliability of the process and how does stiction can occur at different stages of valve opening is still missing to consider. So, there is a need to understand the reliability behavior and effects on the whole process plant and also including the maintenance of the plant. This paper focuses on the prediction of reliability analysis using Weibull distribution and visualizing the maintainability on the bathtub curve to estimate and calculate the life of control valves.

DataDriven Condition Monitoring of Control Valves in Laboratory Test Runs The availability of huge amounts of process data enables datadriven 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 datadriven 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.