Automatic Failure Diagnosis for Flow Control Valves

The increased implementation of digitalisation all over the world has led to an exponential growth of available data across various industries. Consequently, there is a large growth of Machine Learning (ML) techniques being applied to process data. Predictive maintenance is a digital strategy using condition-based monitoring techniques to track the performance of equipment to detect possible defects in advance. In oil refineries, various types of process equipment are used, of which flow control valves are essential to regulate the throughput of heavy, possibly dangerous material. Control valve failures can lead to production loss and increase maintenance costs. This paper addresses the use of time-series data of the valve controller for automated failure diagnosis of flow control valves. Statistical features are extracted from the time series and the significant predictors for the output are adopted in the model using the ANOVA test. The classification and prediction of the failure behaviour are performed using Random Forest (RF) classification. The performance of the diagnosis is measured using the indicators: accuracy and log loss. Findings show that five failure behaviour categories can be predicted, using new and unseen data for the model, with high accuracy(81%).Mechanical Engineerin