Operation monitoring method of pipeline equipment in natural gas compressor station based on spiral microphone array

Abstract

The abnormal operating conditions of pipeline equipment in natural gas compressor station, such as gas pipeline leakage or equipment failure, lead to negative social impacts. To solve the current problem of untimely and inaccurate manual maintenance, this study proposes a 24/7 real-time method for monitoring the operation of pipeline equipment in natural gas compressor station based on a spiral microphone array. First, the Linearly Constrained Minimum Variance (LCMV) beamformer is used to enhance the signal in the target angle direction and suppress the interference in other directions. Then we train the support vector machine (SVM) model based on the station’s Mel-Frequency Cepstral Coefficients (MFCC) feature dataset for diagnosis. The results show that the method is of great feasibility and reliability.