(INVITED)Oil and Gas Pipeline Leakage Recognition Based on Distributed Vibration and Temperature Information Fusion

This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.

Section: Das In the Engineering Sciences (Andrey A Zhirnov And Konsta...mentioning

confidence: 99%

Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective

Gorshkov

Yüksel

Fotiadi

et al. 2022

“…In recent years, many successful trials have been performed [ 87 , 88 , 89 , 90 ]. In 2015, Wu et al applied DAS to pipeline leakage monitoring.…”

Section: Applications Of Das In Linear Infrastructure Monitoringmentioning

confidence: 99%

“…Additionally, when external interference is too strong, false alarms are easily caused. To improve the leakage monitoring capability of DAS, Wang et al proposed the combination of DAS, DTS, and machine learning algorithms to monitor pipeline leakages [ 87 ]. Their experimental results showed that this method can effectively reduce false positives for leakage events.…”

Section: Applications Of Das In Linear Infrastructure Monitoringmentioning

confidence: 99%

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

Zhu

Liu

Wang

et al. 2022

Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

“…After being proposed in 2005 [1], the phase-sensitive optical time-domain reflection technique has been widely used [2] in geological exploration [3][4][5], partial discharge monitoring [6,7], traffic sensing [8,9], marine health monitoring [10], and perimeter security [11,12], etc. It allows long-distance (10 km or more) vibration monitoring on sensing fiber [13,14].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm

Liu

et al. 2022

Self Cite

This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial–temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.