Intelligent Fault Diagnosis of Gas Pressure Regulator Based on AE-GWO-SVM Algorithm

A pressure regulator is essential for pressure control in a gas transmission system. The traditional maintenance approaches for pressure regulators involve equipment disassembly that disrupts normal production. In response, this paper proposes a support vector machine (SVM) model improved by the grey wolf optimization algorithm (GWO) and autoencoder (AE), i.e., the AE-GWO-SVM model. It achieves intelligent fault diagnosis on pressure regulators based on operation data collected by the SCADA system. Firstly, a number of actual pressure regulator faults are counted, and it is found that internal and external leakages are key problems faced by pressure regulators. To address the limited fault data available in field practice, a dataset of pressure regulator faults is constructed using numerical simulation. Additionally, 18 new features are obtained through feature combination, and an improved distance evaluation method (IDE) is utilized to select highly correlated features as input for the machine learning model. Furthermore, an autoencoder (AE) is employed to overcome the interference from abnormal data, which significantly enhances the fault identification process of GWO-SVM. To verify the performance of the AE-GWO-SVM model, an experimental platform for pressure regulators is designed and constructed. Compared with conventional SVM, the accuracy of the AE-GWO-SVM model increases from 60.4% to 86.7%, indicating its strong diagnosis capability for pressure regulator faults.