An Acoustic Fault Diagnosis Method for Oil and Gas Pipelines Based on Time–Frequency Diagrams and Parallel CNN-GRU
Oil and gas pipelines are the core infrastructure of energy transportation, and their safe operation is crucial to national energy security. Aiming at the difficulty of feature extraction and insufficient diagnosis accuracy of pipeline acoustic fault, a fault diagnosis method based on dual-branch parallel feature fusion of the original time-series signal and time–frequency map was proposed. In this method, the time–frequency map of the one-dimensional acoustic signal was generated by continuous wavelet Transform (CWT), and the original signal was input into the dual-branch network, respectively. The spatial–frequency domain features were extracted by using lightweight depthwise separable convolution (LDconv) embedded with coordinate attention (CA) in the upper branch. The lower branch mines local details and temporal dependencies through deformable convolution v4 (DCNv4) and Gated Recurrent Unit (GRU). The dual-branch features were concatenated and fused by Global Average Pooling (GAP), and finally the classification results were output by the fully connected network and Softmax. Experiments on industrial field data show that the average diagnostic accuracy of the proposed method is 98.87%, which can effectively extract weak fault features under complex noise, and has significant advantages in early fault recognition and generalization performance.