Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM

Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings

Minimum entropy deconvolution (MED) is widely used in the gearbox fault diagnosis because it can enhance the energy of the impact signal. However, it is sensitive to single abnormal impulsive oscillation. This is because it takes kurtosis as the objective function and solves the optimal filter by iteration. In addition, the filter length is not adaptive and needs to be determined artificially. This paper proposes a maximum kurtosis spectral entropy deconvolution (MKSED) method and applies it to bearing fault diagnosis. Considering that the kurtosis spectral entropy has the advantage of highlighting the continuous impact oscillation, the kurtosis spectral entropy is chosen as the objective function of deconvolution. At the same time, kurtosis spectral entropy is also used as the fitness function of improved local particle swarm optimization algorithm (LPSO), and the filter length is optimized by LPSO, which makes that MKSED adaptively determines the length of the filter while solving the deconvolution, so that it can accurately extract the continuous pulse signal. The results of the simulation signal analysis show that the proposed MKSED method is superior to MED, and the proposed method is applied to bearing fault diagnosis, which verifies its ability to extract continuous impact.

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A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution

Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time–frequency spectrum

Natural gas leak location with K–L divergence-based adaptive selection of Ensemble Local Mean Decomposition components and high-order ambiguity function

Target location method for pipeline pre-warning system based on HHT and time difference of arrival

Automatic feature extraction is one of the most advantageous merits of deep neural network (DNN), meanwhile, it is an important part for intelligent bearing fault diagnosis. However, most of fault diagnosis methods based on DNN usually excavate the complex relations from original time sequence signals which only present the fault information in time domain. Convolutional Neural Network (CNN) has demonstrated powerful feature learning capabilities in bearing fault diagnosis and the deeper the diagnosis model is, the better the recognition performance is, which resulted in some problems. In order to enrich the fault information from different views and enhance the discrimination for features learned from diagnosis network, this paper proposed a bearing fault diagnosis method based on multi-domain information fusion and improved residual dense network. The original signal and its transformed signals composed the multi-channel input, which contained more comprehensive information and will benefit the deep learning. Then it designed a residual dense network and introduced the convolution attention mechanism which can discriminate the importance of features further improve the feature extraction capability and efficiency of diagnosis network. Finally, it achieved the fault classification, analyzed the effects of key parameters and compared with other diagnosis to verify the effectiveness by lots of experimental results.

Jiedi Sun

Papers

Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM

Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time–frequency spectrum

Natural gas leak location with K–L divergence-based adaptive selection of Ensemble Local Mean Decomposition components and high-order ambiguity function

Target location method for pipeline pre-warning system based on HHT and time difference of arrival

Bearing Fault Diagnosis Based on Multiple Transformation Domain Fusion and Improved Residual Dense Networks