Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Zadkarami, Morteza; Shahbazian, Mehdi; Salahshoor, Karim

doi:10.1016/j.jlp.2016.06.018

Cited by 87 publications

(41 citation statements)

References 10 publications

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“…Among these extensive types of ANNs and their derivations, The multi-layer perceptron (MLP), a feedforward multilayer network with non-linear node functions, is the most commonly encountered one [33,34]. Practically, MLP shows successful generalization capability, effectiveness and efficiency in forecasting time series [10,11,19,23], as well as great compatibility coping with different optimization methods or existing models [19,35]. Although MLP is usually the better choice or at least the same performance with respect to other proposal networks [33], there remain certain delimitations that have a remarkable impact on the training accuracy and efficiency.…”

Section: Type Of Annmentioning

confidence: 99%

“…Simple networks maybe less accurate in learning the problem while complex networks may take excessively long training time. one hidden layer is usually sufficient in most cases [14, 19-25, 33, 41-43] while sometimes multiple hidden layers shows better learning on certain problems [35]. The number of nodes in hidden layer is usually determined through trial-and-error method [19,23,43].…”

Section: The Structure Of Mlp Networkmentioning

confidence: 99%

“…Simple networks maybe less accurate in learning the problem while complex networks may take excessively long training time. one hidden layer is usually sufficient in most cases [14,[19][20][21][22][23][24][25]33,[41][42][43] while sometimes multiple hidden layers shows better learning on certain problems [35].…”

Section: The Structure Of Mlp Networkmentioning

confidence: 99%

See 2 more Smart Citations

Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Zhang

Urich

et al. 2018

Sustainability

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In this study, the neural network method (Multi-Layer Perceptron, MLP) was integrated with an explorative model, to study the feasibility of using machine learning to reduce the exploration time but providing the same support in long-term water system adaptation planning. The specific network structure and training pattern were determined through a comprehensive statistical trial-and-error (considering the distribution of errors). The network was applied to the case study in Scotchman’s Creek, Melbourne. The network was trained with the first 10% of the exploration data, validated with the following 5% and tested on the rest. The overall root-mean-square-error between the entire observed data and the predicted data is 10.5722, slightly higher than the validation result (9.7961), suggesting that the proposed trial-and-error method is reliable. The designed MLP showed good performance dealing with spatial randomness from decentralized strategies. The adoption of MLP-supported planning may overestimate the performance of candidate urban water systems. By adopting the safety coefficient, a multiplicator or exponent calculated by observed data and predicted data in the validation process, the overestimation problem can be controlled in an acceptable range and have few impacts on final decision making.

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Section: Type Of Annmentioning

confidence: 99%

Section: The Structure Of Mlp Networkmentioning

confidence: 99%

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Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Zhang

Urich

et al. 2018

Sustainability

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“…Among these techniques, fiber optic sensors emerged as the most appropriate for long distance pipelines with no potential safety hazard compared with traditional electrical gauges. From the aspect of algorithms, the fluid transient state due to leakage occurrence is always analyzed, then the leak accidents can be identified, thus locating the leak point, in combination with signal processing techniques and mathematic analysis, including artificial neural network [7,8], support vector machine (SVM) [9,10], harmonic wavelet analysis [11], 2 of 13 etc. Until now, no integrated solution for leakage detection combining both advantages of the fiber sensor and advanced algorithm has been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Leak Localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network

Jia

Ren

et al. 2018

Applied Sciences

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Featured Application: A pipeline leakage localization method using self-developed FBG hoop strain sensors combined with BP neural network is proposed and validated. Abstract: Pipelines function as blood vessels serving to bring life-necessities, so their safe usage is one of the foremost concerns. In our previous work, a fiber Bragg grating (FBG) hoop strain sensor with enhanced sensitivity was developed to measure the pressure drop induced by pipeline leakage. Some hoop strain information during the leakage transient process can be extracted from the amount of FBG hoop strain sensors set along the pipeline. In this paper, an integrated approach of a back-propagation (BP) neural network and hoop strain measurement is first proposed to locate the leak points of the pipeline. Five hoop strain variations are employed as input neurons to achieve pattern recognition so as to predict the leakage point. The RMS error can be as low as 1.01% when choosing appropriate hidden layer neurons. Furthermore, the influence of noise on the network's performance is investigated through superimposing Gaussian noise with a different level. The results demonstrate the feasibility and robustness of the neural network for pipeline leakage localization.

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“…In response to this problem, some researches on pipeline detection have been proposed in domestic and international [4]. The optical fiber pre-warning system (OFPS) is often used for disasters occurrence monitoring such as oil and gas pipeline leakage, and it is mainly used for detection and recognition of intrusion source [5,6]. Some documents proposed they found an effective way to set the soft or hard thresholds for every point along the fiber adaptively to improve the signal-to-noise ratio (SNR) [7,8].…”

Section: Introductionmentioning

confidence: 99%

RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

et al. 2018

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The optical fiber pre-warning system (OFPS) has been gradually considered as one of the important means for pipeline safety monitoring. Intrusion signal types are correctly identified which could reduce the cost of troubleshooting and maintenance of the pipeline. Most of the previous feature extraction methods in OFPS are usually quested from the view of time domain. However, in some cases, there is no distinguishing feature in the time domain. In the paper, firstly, the intrusion signal features of the running, digging, and pick mattock are extracted in the frequency domain by multi-level wavelet decomposition, that is, the intrusion signals are decomposed into five bands. Secondly, the average energy ratio of different frequency bands is obtained, which is considered as the feature of each intrusion type. Finally, the feature samples are sent into the random vector functional-link (RVFL) network for training to complete the classification and identification of the signals. Experimental results show that the algorithm can correctly distinguish the different intrusion signals and achieve higher recognition rate.

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Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Cited by 87 publications

References 10 publications

Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Pipeline Leak Localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network

RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

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