Optimized PSOMV-VMD combined with ConvFormer model: A novel gas pipeline leakage detection method based on low sensitivity acoustic signals

Traditional acoustic leak detection methods rely on artificial sensor systems and are expensive to implement. The signals collected by pipeline leak detection robots based on low-cost microphone arrays have low signal-to-noise ratios and are difficult to capture signal details, which affects the detection results. Therefore, this paper introduces a cost-effective method for gas pipeline leakage detection using a combination of optimized Variational Mode Decomposition (VMD) and the ConvFormer model. The optimized VMD reduces noise in low-sensitivity acoustic signals, enhancing feature extraction. The ConvFormer model then processes the spectrogram to detect leaks. Leakage experiments conducted on a 100 m gas pipeline validated the method's effectiveness. Results demonstrate a significant improvement in noise reduction, with reductions in Mean Squared Error (MSE) and Mean Absolute Error (MAE) by 20 %–30 % and 18 %–24 %, respectively. The method achieved a high detection accuracy of 99.31 %, offering a reliable solution for intelligent pipeline inspection robots.