A Pipeline Leakage Identification Method Based on Improved Self-Attention Mechanism by High-Low Frequency Differential

Oil pipelines are one of the most important modes of modern energy transportation, and due to many factors, such as deterioration, aging, natural disasters and third-party sabotage in the operation of pipelines, leakage accidents and other potential safety hazards are frequent, posing a serious threat to the safety of the energy supply system. In order to meet this challenge, wireless sensor networks are often used to implement online and real-time monitoring of oil pipelines, which can compensate for the shortcomings of low efficiency of manual inspection and the lack of accuracy of existing leak diagnosis technologies in terms of small leaks and multi-source leaks. However, factors such as measurement noise interference, differences in sensor types, limitations in the number of network nodes, and monitoring location layout will lead to the problems of diverse forms, large quantities, and complex relationships of the collected detection information. In order to ensure that the type of pipeline leakage can be accurately judged in a timely manner, the information fusion method should be adjusted to achieve the functions of signal noise reduction, multi-sensor information fusion and complementarity, and joint decision-making of multi-sensor identification results [1]. In this paper, we propose a method for improving the identification of pipe leakage types using High and Low Frequency Difference (HLFD) to improve the self-attention mechanism driven Long Short-Term Memory Network (LSTM) for multi-source data. Based on the structural characteristics of the pipeline leakage monitoring network system and the in-depth analysis of the characteristics of the detection data, this paper constructs a neural network model based on the HLFD method of self-attention driven LSTM, which uses the feature-level multi-source data fusion method. In the initial stage of data processing, we use wavelet transform technology to pre-process the leakage signal and extract key data features. Subsequently, at the feature processing level, we use the improved self-attention-driven LSTM model to comprehensively analyze the preliminary recognition results from multiple nodes to achieve more accurate joint decision-making. Firstly, the high-dimensional feature vectors were constructed by extracting the features of the pressure data under different working conditions. Subsequently, the self-attention driven LSTM model was used to train the pressure change data. Finally, the abnormal pressure data and flow data identified by classification are used to identify the specific working conditions, so as to realize the leakage alarm function. This method not only improves the accuracy of pipeline leakage identification, but also provides effective support for timely response to potential leakage risks.