Leakage fault diagnosis of oil and gas pipelines based on improved spiking residual network

The safe operation of oil and gas pipelines is of vital importance for maintaining national energy security. Therefore, the implementation of efficient pipeline leakage detection is an important link to ensure the safe and stable operation of pipelines. In this paper, a pipeline leakage detection method based on an improved spiking residual network is proposed. First, a coding method is proposed to encode the original signal into a spiking sequence. The input oil and gas pipeline signals are encoded using short-time Fourier transform combined with spatial gating mechanism and LIF neurons. Second, wavelet convolution was introduced to improve the original spiking residual network. Finally, the improved spiking residual network is used to classify the pipeline signals after the coding process. The experimental results show that the classification accuracy of the model proposed in this paper reaches 100 % on the original signal data, and 95.62 % with the addition of 5 dB Gaussian white noise, which effectively shows that the method has high accuracy and strong robustness, and can effectively improve the oil and gas pipeline leakage detection effect.