Study on vibration signals identification method for pipeline leakage detection based on deep learning technology

Abstract

For natural gas pipeline leakage systems based on distributed feedback fiber laser vibration sensors (DFB-FL), a novel neural network model (mCNN-LFLBs) integrating multi-scale convolutional neural networks (mCNN) with local feature extraction blocks (LFLBs) is proposed in this study. To address the problem of feature loss in the traditional signal image transfer process, the one-dimensional original signal is directly used as the input of the model, effectively maintaining the time-domain correlation of the leaked signal. The time-domain correlation of the leakage signal is preserved. To address the problem that traditional pattern recognition algorithms have a weak ability to extract deep features and local features, mCNN is established by this model, which realizes the use of convolution kernels of different scales to extract features of different frequency bands in one-dimensional signals. Additionally, LFLBs are introduced, which enhances the local expression ability of the model, further improves the feature mining quality of the model, and better presents the deep features of leaked signals. Experimental results demonstrate that the mCNN-LFLBs model has a classification accuracy of up to 98.6%, with a testing response time of approximately 10 ms, and outstanding performance in terms of convergence speed. These results highlight the potential practical application of the mCNN-LFLBs model in natural gas leak detection.