Fault Diagnosis of Gas Pressure Regulators Based on CEEMDAN and Feature Clustering

Abstract

Gas pressure regulators are widely applied in natural gas pipeline networks, correspondingly, establishing an efficient fault diagnosis approach of regulators plays a critical role in optimizing the safety and reliability of pipeline network systems. In our paper, considering that the outlet pressure signals of gas regulators are nonstationary and nonlinear, we propose a fault diagnosis approach combining a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and fuzzy ${c}$ -means (FCM) clustering to classify three typical faults of gas regulators. First, we propose to apply the CEEMDAN approach for decomposing intrinsic mode functions (IMFs). Then feature vectors of the typical faults are established by Hilbert marginal spectrum (HMS) of IMFs. Finally, we adopt cluster centers and feature clustering algorithm to distinguish the types of faults. The experimental results indicate the high performance of the present fault diagnosis approach. The membership degrees of test samples obtained from the CEEMDAN algorithm are optimized to be within 0.9 to 1.