Research on oil and gas pipeline leak detection method based on 1DCNN-DBO-LSTM

Abstract Leak detection of oil and gas pipelines is essential for safe pipeline operation. Existing methods for detecting oil and gas pipeline leaks are inadequate and do not exploit the temporal characteristics of the leak signal. In this paper, a fusion recognition model of a long short-term memory network (LSTM) optimized by a one-dimensional convolutional neural network (1DCNN) and dung beetle optimization algorithm (DBO) is proposed. First, the one-dimensional signal is input into the 1DCNN. Then the adaptively extracted data features from the 1DCNN are input into the optimized DBO-LSTM for classification. The model is evaluated using the precision and duration metrics. In comparison to extant advanced models, the proposed model improves recognition precision and decreases detection time. The model proposed in this paper is able to extract pipeline data features more rapidly and precisely, thereby improving classification accuracy.