Leakage aperture recognition based on ensemble local mean decomposition and sparse representation for classification of natural gas pipeline

Sun, Junhua; Peng, Zhitao; Wen, Jia

doi:10.1016/j.measurement.2017.05.029

Cited by 28 publications

(13 citation statements)

References 31 publications

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“…Based on the obtained kurtosis features, the principal product function components with higher leak information content were chosen for further processing. Sun et al proposed a hybrid ensemble local mean decomposition (ELMD) and sparse representation for recognition of leakage orifices in a natural gas pipeline [31]. In that study, an ELMD scheme was employed to perform adaptive decomposition of the leak signatures and acquisition of information feature of the leak signal based on different orifice scenarios.…”

Section: Interior/computational Methodsmentioning

confidence: 99%

“…Several pipeline leak detection methods have been proposed during the last decades using different working principles and approaches. Existing leakage detection methods are: acoustic emission [13,14,15], fibre optic sensor [16,17,18], ground penetration radar [19,20], negative pressure wave [21,22,23], pressure point analysis [24,25,26], dynamic modelling [27,28], vapour sampling, infrared thermography, digital signal processing and mass-volume balance [29,30,31,32,33]. These methods have been classified using various frameworks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Adegboye

Fung

Karnik

2019

Sensors

294

144

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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.

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Section: Interior/computational Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Adegboye

Fung

Karnik

2019

Sensors

294

144

View full text Add to dashboard Cite

show abstract

“…Sun et al [122] used the LMD and envelope spectrum entropy to extract fault features of a pipeline system and then classified various health conditions using SVM. Li et al [123] [128] adopted ELMD and sparse representation for fault classification of natural gas pipelines. Si et al [129] proposed an intelligent method based on LMD, Laplacian score (LS)…”

Section: Applications Using Lmd-based Combination Methodsmentioning

confidence: 99%

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Liu

et al. 2019

Journal of Systems Engineering and Electronics

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Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts: fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.

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“…According to [26] ELMD can be described by the following steps: (1. ) Adding white noise to the signal x(t) thus forming y(t).…”

Section: Ensemble Local Mean Decompositionmentioning

confidence: 99%

Genetic algorithms to determine the optimal parameters of an ensemble local mean decomposition

Silva

Borges

2021

Acta Polytech

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An optimization method for an ensemble local mean decomposition (ELMD) parameters selection using genetic algorithms is proposed. The execution of this technique depends heavily on the correct choice of the parameters of its model as pointed out in previous works. The effectiveness of the proposed method was evaluated using synthetic signals, discussed by several authors. The resulting algorithm obtained similar results to OELMD, but with an 82% reduction in processing time. Actual vibration signals were also analysed, presenting satisfactory results.

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Leakage aperture recognition based on ensemble local mean decomposition and sparse representation for classification of natural gas pipeline

Cited by 28 publications

References 31 publications

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

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

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Genetic algorithms to determine the optimal parameters of an ensemble local mean decomposition

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