Pipeline safety early warning (PSEW) systems based on distributed optical fiber sensors are used to recognize and locate third-party events that may damage long-distance energy transportation pipelines and are essential to ensure pipeline safety and energy supply. However, the deployment of PSEW systems in real sites is hindered by the high experimental cost of collecting large real-site data sets for model building and the small percentage of labeled data (typically less than 0.5%). Besides, the optical fiber sensors are sensitive to hardware and the environment, ensuring challenges to directly migrate the old PSEW system for a new deployment. In this study, a novel semi-supervised learning model is proposed to monitor the safety of pipelines in real-time. Concretely, the sparse stacked autoencoder trained with unlabeled data is used to extract more robust features, and the fully-connected network trained with a small amount of labeled data is used for location and identification. Encouraging empirical results on the real-world long-distance energy pipelines of the PipeChina confirm that our method achieves better recognition and localization performance in comparison to the baseline with less labeled data. Further, the model size and recognition latency are reduced by 18.9× and 7.9× of the baseline, respectively. Also, the decoded features have better visualization than the input. This work reduces the cost of PSEW system deployments, improves its performance and portability, and will contribute to the widespread use of PSEW systems in the industry.

This study presents a new concept design combining multiple megawatt (MW) vertical-axis wind turbines (VAWTs) and a solar array with a floating steel fish-farming cage. This combined wind–solar–aquaculture (WSA) system is intended to utilize the ocean space and water resources more effectively and more economically, while greatly shortening the payback period of investment in offshore power generation. The details of this WSA design are described, showing that a square-shaped fishing cage serves as a floating foundation for the 7600 m² solar array and four multi MW VAWTs. The WAMIT program based on potential-flow theory is employed to obtain the WSA’s motion response amplitude operators (RAOs) in sinusoidal waves of varying periods. The motion RAOs indicated that the proposed concept possesses better hydrodynamic seakeeping performances than its OC3Hywind spar and OC4DeepCwind semi-submersible counterparts. A potential site located in the northwest South China Sea is selected to deploy the WSA. Its feasibility is then examined in terms of the hydrodynamic motions and structural dynamic response driven by wind, waves, and current. Fully coupled time-domain simulations are carried out for 50-year survival conditions. The whole structure exhibits outstanding performance for its small motions in random wind and seas. Moreover, under these survival conditions, the top accelerations and tower base stresses of the VAWTs and mooring line tensions readily meet the design requirements. Technically, the WSA has strong competitiveness and wide prospects in the offshore industry for both power exploitation and marine aquaculture in intermediate and deep waters.

A novel spectrum differential integration (SDI) method has been proposed and verified in an in-line fiber Mach-Zehnder (MZ) refractive index (RI) sensor using salt solutions. In SDI method, the difference between two interference spectra is determined by pointwise subtraction at each wavelength, followed by integration of the absolute differences along the scan range. Compared with the widely used peak wavelength shift method, the SDI method is more reliable over a wide wavelength range (on the order of 400 nm) and results in higher sensitivity as well as reduced device-dependence. The SDI method can also be utilized with other kinds of modal interferometric sensors.

In modern society, the oil industry has become the foundation of the world economy, and how to efficiently extract oil is a pressing problem. Among them, the accurate measurement of oil-gas two-phase parameters is one of the bottlenecks in oil extraction technology. It is found that through the experiment the flow patterns of the oil-gas two-phase flow will change after passing through the venturi tube with the same flow rates. Under the different oil-gas flow rate, the change will be diverse. Being motivated by the above experiments, we use the dual ECT sensors to collect the capacitance values before and after the venturi tube, respectively. Additionally, we use the linear projection algorithm (LBP) algorithm to reconstruct the image of flow patterns. This paper discusses the relationship between the change of flow patterns and the flow rates. Furthermore, a convolutional neural network (CNN) algorithm is proposed to predict the oil flow rate, gas flow rate, and GVF (gas void fraction, especially referring to sectional gas fraction) of the two-phase flow. We use ElasticNet regression as the loss function to effectively avoid possible overfitting problems. In actual experiments, we compare the Typical-ECT-imaging-based-GVF algorithm and SVM (Support Vector Machine) algorithm with CNN algorithm based on three different ECT datasets. Three different sets of ECT data are used to predict the gas flow rate, oil flow rate, and GVF, and they are respectively using the venturi front-based ECT data only, while using the venturi behind-based ECT data and using both these data.

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

Energy pipelines are the backbones of global energy systems. Monitoring their safety and automatically identifying and locating third-party damage events are crucial to energy supply. However, most traditional methods lack in-depth consideration of distributed fiber signals and have not been tested on real-world long-distance pipelines, making it difficult to deploy them in operating long-distance pipelines. In this study, we utilize a novel real-time machine-learning method based on phase-sensitive optical time domain reflectometer technology to monitor the safety of oil and gas pipelines. Specifically, we build a multifeature-fusion convolutional neural network and LightGBM fusion model based on two novel complementary spatiotemporal features. The method was applied to a large amount of data collected from real-world oil–gas transportation pipelines of the China National Petroleum Corporation. The proposed method could accurately locate and identify third-party damage events in real-time under conditions of strong noise and various types of system hardware, and could effectively handle signal drift in the time and space dimensions. Our methodology has been deployed at real long-distance energy pipeline sites and our work will contribute to energy pipeline safety and energy supply security. Furthermore, the proposed solution could be generalized to other fields, such as industrial inspection, measurement, and monitoring.