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• W4366245593 abstract "Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses, a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents. The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering. Current oil pipeline leakage signals are insufficient for feature extraction, while the training time for traditional leakage prediction models is too long. A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt (GA-LM) classification model for predicting the leakage status of oil pipelines. The signal that has been processed is transformed to the time and frequency domain, allowing full expression of the original signal. The traditional Back Propagation (BP) neural network is optimized by the Genetic Algorithm (GA) and Levenberg Marquardt (LM) algorithms. The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter. The Accuracy, Precision, Recall, and F1score of the GA-LM model is 95%, 93.5%, 96.7%, and 95.1%, respectively, which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples. The proposed GA-LM model can obviously reduce training time and improve recognition efficiency. In addition, considering that a large number of samples are required for model training, a wavelet threshold method is proposed to generate sample data with higher reliability. The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines." @default.
• W4366245593 created "2023-04-20" @default.
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• W4366245593 date "2023-04-01" @default.
• W4366245593 modified "2023-09-30" @default.
• W4366245593 title "A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features" @default.
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• W4366245593 doi "https://doi.org/10.1016/j.petsci.2023.04.016" @default.
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