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Gathering pipelines are one of the key upstream infrastructures in the gas industry that link production well to the processing plant. Leak detection is critical for ensuring the safety of pipeline transmission. The detection of small leakage in gathering pipelines consistently poses a formidable challenge. In this paper, a process model is built based on health data of supervisory control and data acquisition system from the actual operating pipeline. In the model structure, the convolutional neural network is used to extract the spatial features, the bi-directional long short-term memory is used to extract the temporal features, and the attention mechanism is employed to allocate the model's attention resources reasonably. Next, the residual between the entity pipeline's output data and the process model's output data is used as a monitoring indicator of the operating state of the pipeline. A clustering-based boundary determination method is proposed to recognize the centroid of normal and small leak conditions, and pipeline leak detection is performed by the Euclidean distance between the monitoring indicator and the centroid. This paper explores the feasibility of fast modeling and leak detection with limited hardware. Field tests for the validation of the proposed methods were implemented in two in-service natural gas gathering pipeline. The experimental results demonstrate that the proposed method significantly enhances the detection performance of small-size leak. The leak detection rates of 94.06% and 92.16% evinces the potency of the proposed method applied in the leak detection of gathering pipelines across diverse real-world scenarios. [ABSTRACT FROM AUTHOR]

Gathering pipelines are one of the key upstream infrastructures in the gas industry that link production well to the processing plant. Leak detection is critical for ensuring the safety of pipeline transmission. The detection of small leakage in gathering pipelines consistently poses a formidable challenge. In this paper, a process model is built based on health data of supervisory control and data acquisition system from the actual operating pipeline. In the model structure, the convolutional neural network is used to extract the spatial features, the bi-directional long short-term memory is used to extract the temporal features, and the attention mechanism is employed to allocate the model's attention resources reasonably. Next, the residual between the entity pipeline's output data and the process model's output data is used as a monitoring indicator of the operating state of the pipeline. A clustering-based boundary determination method is proposed to recognize the centroid of normal and small leak conditions, and pipeline leak detection is performed by the Euclidean distance between the monitoring indicator and the centroid. This paper explores the feasibility of fast modeling and leak detection with limited hardware. Field tests for the validation of the proposed methods were implemented in two in-service natural gas gathering pipeline. The experimental results demonstrate that the proposed method significantly enhances the detection performance of small-size leak. The leak detection rates of 94.06% and 92.16% evinces the potency of the proposed method applied in the leak detection of gathering pipelines across diverse real-world scenarios. [ABSTRACT FROM AUTHOR]

Leak detection in natural gas pipelines is an extremely important and persistent problem in the oil and gas industry. The construction of accurate physical models of pipelines is limited by the high complexity, unavailable closed-form solution, and strict experienced personnel requirements. Moreover, industrial automation has the common problems of large amount of data with little information. This paper proposes a data-driven digital twin (DT) method for leak detection as a new paradigm solution to these challenges. From the perspective of knowledge-based data-driven, a DT pipeline learning and updating scheme based on normal data directly from operational data during the entity pipelines life-cycle is proposed to enhance DT adaptability. A DT-driven leak detection method is proposed, making effective use of data interaction and fusion of DT. The effectiveness and performance of the proposed approach is illustrated by deploying the DT pipeline in a simulated leak scenario of a real running natural gas pipeline. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd [ABSTRACT FROM AUTHOR]

Automatic Identification System (AIS) plays an essential role in maritime surveillance and the global shipping industry. Thanks to Artificial Intelligence (AI) advances, almost all marine-related application needs can be realized by mining AIS Big Data. Most of the research, however, is still developing an AIS-based AI model for a specific task. When facing complex maritime applications, a large number of AI models are required. How to deploy these single-function AI models has become extremely challenging. Considering the distinctive characteristics of AIS data, this paper develops an AIS-based deep learning model for forecasting, classification, anomaly detection, and imputation in the maritime industry. This paper uses hierarchical blocks to ensure the expressive power and generalization ability, employs signal decomposition methods and processes the decomposed data like image processing. Finally, experiments have been conducted in both publicly available datasets and the dataset established in this paper. The performance of state-of-the-art models has been compared with the proposed model and the results shows that our approach can yield optimal results, including the optimal mean squared error in the forecasting task and imputation task, the accuracy in the anomaly detection task, and the category accuracy in the classification task, on our curated dataset. • We created a labeled dataset on vessel traffic in the upper reaches of the Yangtze River by comparing data through standard screening, trajectory tracing, and manual verification. • To minimize redundancy in AIS sequence information, we employ a whitening extraction method to capture vessel behavior modes. • This paper propose a "Meta" model to cater to different task requirements concerning AIS data. [ABSTRACT FROM AUTHOR]

In microwave heating applications, Lambert’s law is a common way to calculate power distribution. However, because of the complex application environment, Lambert’s law is not precise for the unknown power distribution on material surfaces. During the microwave heating process, the system process parameters can only be partly known by experience. Therefore, for such situations, to make the entire heating process safe, a sliding mode combined with a neural network algorithm is proposed. The algorithm is designed to calculate the suitable input power at each control period to make the material temperature follow the reference trajectory, which is determined by experience. The simulation and actual application results demonstrate that the proposed algorithm can commendably control the heating process. The difference between the reference trajectory and the material sampling temperature may exceed 1°C initially. However, as time progresses, the difference gradually decreases. Nonetheless, due to the low conduction coefficient, a single microwave heating process may take a long time. Therefore, many actual applications combine convective heat transfer with microwave. This article also discusses the control method of multiple inputs including microwave power and convective heat transfer with unknown model parameters. Another neural network is constructed to identify the unknown parameters. The algorithm is designed to obtain the suitable input power and input convective heat transfer at each control period. The simulation results show that the control algorithm can work well under multiple inputs. The material temperature on both the surfaces and the interior can follow the reference trajectory with a satisfactory difference, and suitable inputs can be obtained with few fluctuations during the learning process. [ABSTRACT FROM PUBLISHER]