Jet fuel pipeline leakage will cause environmental pollution and safety accidents, so the leak detection of jet fuel pipeline is a very important part of pipeline management. Compared with negative pressure wave, acoustic wave has better attenuation resistance and longer propagation distance. However, acoustic waves are easily disturbed by noise, making the acoustic signal mixed with a large amount of noise, thereby reducing the accuracy of the detection system to identify pipeline leaks. In this paper, an improved uniform phase empirical mode decomposition (IUPEMD) denoising method is proposed. Compared with other denoising methods, intrinsic modal function (IMF) with more leakage information can be selected according to the similarity coefficient for signal reconstruction. The reconstructed signal retains the leak information to a greater extent, and the noise content is extremely low, which can effectively improve the leak identification accuracy of the leak detection system. In order to accurately determine the leakage of pipeline and solve the problem of low accuracy of recognition model, this paper establishes a deep learning twin support vector machine (DTWSVM) for identifying the state of pipeline based on deep learning (DL) and twin support vector machine (TWSVM), which can automatically extract the leakage feature information and accurately determine the leakage of pipeline based on the feature information. The experimental analysis shows that the IUPEMD denoising method can effectively filter the noise in the signal, and the recognition accuracy of the DTWSVM model is very high, and its leakage recognition accuracy can reach 99.6%.
Oil pipeline leakage will not only cause economic losses, but also pollute the environment, so the leakage detection of pipelines is very important. The acoustic wave method is widely used in pipeline leak detection, and the leak acoustic signal collected by the acoustic wave sensor often contains a lot of noise, which makes it impossible to accurately determine the inflection point of the signal curve and reduces the accuracy of pipeline leak detection. This paper proposes a denoising algorithm based on mutual information optimization complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with cross-spectral analysis. Compared with other methods, this method can accurately select the effective intrinsic modal function (IMF) for signal reconstruction, the denoising effect is more obvious, and the original information is preserved to a greater extent. Acoustic waves are attenuated during the propagation process, and will be affected by factors such as pipe connection ports and elbows, making it impossible to accurately determine the amplitude of acoustic waves around the pipeline. According to the propagation characteristics of acoustic waves and various factors that affect the propagation of acoustic waves, this paper establishes a model for calculating the amplitude of acoustic waves, which can accurately determine the amplitude of acoustic waves everywhere in the pipeline. Finally, according to the model, the relationship between pipeline characteristics and detectable leakage rate is analysed. Field experiments show that the proposed model is accurate and the denoising algorithm is efficient. The minimum detectable leakage rate of the oil pipeline can reach 0.43% when the acoustic wave method is used for leak detection.
To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was decomposed into several intrinsic scale components (ISC) by the UPLCD algorithm. Then, the signal reconstruction process of UPLCD was optimized and improved according to the energy and standard deviation of the amplitude of each ISC, the ISC components dominated by the signal were selected for signal reconstruction, and the denoised signal was obtained. Finally, the TBSVM was optimized using a grid search algorithm, and a GS-TBSVM model for pipeline leakage identification was constructed. The input of the GS-TBSVM model was the data processed by the IUPLCD algorithm, and the output was the real-time working conditions of the gas pipeline. The experimental results show that IUPLCD can effectively filter the noise in the signal and GS-TBSVM can accurately judge the working conditions of the gas pipeline, with a maximum identification accuracy of 98.4%.
When there is a small leak in the oil pipeline, their susceptibility to the influence of external noise prevents their detection based on the robust principal component analysis (RPCA) method, which does not consider the matrix of dense noise. Thus, to solve this problem, leak detection and localization method based on an improved robust principal component analysis (IRPCA) for pipelines is proposed. By solving a convex optimization function, this method can remove the dense noise contained in the collected data and improve the efficiency of small leak detection. In addition, the collected leakage data is analyzed. Leak detection is monitored by the combined index D2, which is a combined indicator that is composed of Hotelling’s T2 statistic and the SPE statistic. The experimental results show that the missing and false leak detection accuracies of the combined index D2 are much higher than those of the Hotelling’s T2 statistic and the SPE statistic separately. Furthermore, it verifies that the small leak localization method proposed in this paper has a good effect.
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