Leak Detection and Location Based on ISLMD and CNN in a Pipeline

Zhou, Mengfei; Pan, Zheng; Liu, Yunwen; Zhang, Qiang; Cai, Yijun; Pan, Haitian

doi:10.1109/access.2019.2902711

Cited by 66 publications

(28 citation statements)

References 19 publications

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“…In every fault diagnosis method feature extraction is an important part. In the literature, diverse statistical techniques are used to extract time-domain features for leak diagnosis purposes (Lang et al, 2017; Song and Li, 2018; Zhou et al, 2019). Table 5 demonstrates the statistical features that are employed in this paper.…”

Section: The Proposed Methodologymentioning

confidence: 99%

Data driven leakage diagnosis for oil pipelines: An integrated approach of factor analysis and deep neural network classifier

Zadkarami

Safavi

Taheri

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper proposes a novel data-based leakage diagnosis method for big datasets, which identifies the leak occurrence, its size, and its location. Different statistical features are used to express the changes in flow and pressure signals at different leakage scenarios. To improve the performances of the leakage diagnosis approach, factor analysis (FA) is employed for dimension reduction purposes. The optimal features of both pressure and flow signals are then fed as input vectors to a deep neural network (DNN) classifier. The proposed leakage diagnosis method has been applied to the first 20 km of the Golkhari-Binak oil pipeline, located in Iran. The leakage isolation accuracy has been compared with some related works. Simulation results show that the proposed method significantly outperforms others with the average correct classification rate (CCR) of about 98%.

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Section: The Proposed Methodologymentioning

confidence: 99%

Data driven leakage diagnosis for oil pipelines: An integrated approach of factor analysis and deep neural network classifier

Zadkarami

Safavi

Taheri

et al. 2020

Transactions of the Institute of Measurement and Control

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show abstract

“…At the same time, the use of the GPU-based Compute Unified Device Architecture (CUDA) greatly accelerates the training speed of neural networks. Based on the above advantages, AlexNet has been applied in defect detection, location and visual tracking of dynamic objects [85,86]. In 2014, the GoogLeNet network proposed by Google [87] won the ILSVRC competition, and its error rate was lower than that of VGGNet proposed in the same year.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

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With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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“…In recent years, machine learning methods have been increasingly used for leakage detection and localization. Zhou et al [19] and Pérez-Pérez et al [20] investigated leak detection in a single pipeline. In the Zhou et al [19]'s work, a convolutional neural network (CNN) was used to pinpoint leak locations in a 1500 m long pipe segment for different leak sizes, where the better prediction was obtained for greater leakages.…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al [19] and Pérez-Pérez et al [20] investigated leak detection in a single pipeline. In the Zhou et al [19]'s work, a convolutional neural network (CNN) was used to pinpoint leak locations in a 1500 m long pipe segment for different leak sizes, where the better prediction was obtained for greater leakages. Pérez-Pérez et al [20] used a combined artificial neural network (ANN), where the ANN is first used to estimate the friction factor of the pipe and then to localize leak location.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

Lučin

Čarija

et al. 2021

Mathematics

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In the present paper, a Random Forest classifier is used to detect leak locations on two different sized water distribution networks with sparse sensor placement. A great number of leak scenarios were simulated with Monte Carlo determined leak parameters (leak location and emitter coefficient). In order to account for demand variations that occur on a daily basis and to obtain a larger dataset, scenarios were simulated with random base demand increments or reductions for each network node. Classifier accuracy was assessed for different sensor layouts and numbers of sensors. Multiple prediction models were constructed for differently sized leakage and demand range variations in order to investigate model accuracy under various conditions. Results indicate that the prediction model provides the greatest accuracy for the largest leaks, with the smallest variation in base demand (62% accuracy for greater- and 82% for smaller-sized networks, for the largest considered leak size and a base demand variation of ±2.5%). However, even for small leaks and the greatest base demand variations, the prediction model provided considerable accuracy, especially when localizing the sources of leaks when the true leak node and neighbor nodes were considered (for a smaller-sized network and a base demand of variation ±20% the model accuracy increased from 44% to 89% when top five nodes with greatest probability were considered, and for a greater-sized network with a base demand variation of ±10% the accuracy increased from 36% to 77%).

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Leak Detection and Location Based on ISLMD and CNN in a Pipeline

Cited by 66 publications

References 19 publications

Data driven leakage diagnosis for oil pipelines: An integrated approach of factor analysis and deep neural network classifier

Data driven leakage diagnosis for oil pipelines: An integrated approach of factor analysis and deep neural network classifier

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

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