Explore the vast range of titles available.

The overlying rock layers in a mining area may collapse or settle, subjecting pipelines to uneven forces that can lead to deformation or even fracture. This paper proposes a pipeline safety monitoring method that combines fiberoptic vibration and strain sensing to detect vibrations and deformations caused by rock layer collapse in mining zones. First, pipeline deformation monitoring under unknown force directions was investigated using fiber Bragg grating (FBG) sensing technology. Second, we constructed a mining area pipeline model and conducted vibration/deformation monitoring tests employing FBG sensors, distributed Brillouin strain sensing, and distributed fiberoptic vibration sensing technologies. The experimental results demonstrate that FBG sensor arrays deployed at 90-degree intervals can effectively identify the pipeline’s primary force direction and maximum strain, with direction angle errors of less than 5.2%. The integrated analysis of vibration and strain data enables accurate identification and measurement of extended vibration responses and pipeline deformations in open-air zones. This study establishes a comprehensive monitoring framework for ensuring pipeline safety in mining areas.

A mechanical structure of a fiber Fabry–Perot interferometric vibration sensor for monitoring oil and gas pipelines has been proposed, and design analysis research on performance improvement has been carried out. By designing a serpentine beam structure, the mechanical sensitivity of the sensor is enhanced. Meanwhile, by designing a vertically symmetrical gravity-sensing structure, the cross-axis sensitivity of the sensor is reduced. The results of simulation analysis show that the mechanical sensitivity of the proposed design structure is 89.20 μm/g, which is 32.44 times that of the conventional structure. Moreover, due to the design of low cross-axis sensitivity, the optical sensitivity of the vibration sensor will not be degraded because of its installation status on the pipeline. The proposed mechanical structure provides a design reference for the application of the fiber Fabry–Perot interferometric vibration sensor on oil and gas pipelines, and offers potential for the development of a high-performance comprehensive safety risk monitoring system for oil and gas pipelines.

Oil and gas pipelines are of great importance in China, and pipeline security problems pose a serious threat to society and the environment. Pipeline safety has therefore become an integral part of the entire national economy. Landslides are the most harmful type of pipeline accident, and have directed increasing public attention to safety issues. Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards, there remains a need for effective monitoring and early warning methods, especially when the complexity of pipeline-landslides is considered. Because oil and gas pipeline-landslides typically occur in the superficial soil layers, monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline. To address the particular characteristics of pipeline-landslides, we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment. In this paper, we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms. The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China. The results presented here provide guidelines for the monitoring, early warning, and prevention of pipeline geological hazards.

Oil and gas transmission pipelines require monitoring for maintenance and safety, to prevent equipment failure and accidents. Unmanned aerial vehicles (UAVs) technology is emerging as an opportunity to supplement current monitoring systems. Small UAV technological solutions are flexible and adaptable and with a demonstrated capacity to obtain valuable data at small to medium spatial scales. Systematic surveys of extensive areas are better completed with fixed-wing platforms and automatic flight design, whilst multirotor platforms provide flexibility in shorter and localized inspection missions. The type of sensor carried by an aerial platform determines the sort of data acquired and the obtainable information; sensors also determine the need for specific mechanical designs and the provision of energy on-board required from the system. UAV systems prototyped to monitor pipelines are reviewed in this paper, and a number of monitoring scenarios are proposed and illustrated. Notwithstanding difficulties encountered in the generalization of use for civilian applications, small UAVs have demonstrated, through research and operational cases, the capacity to support the inspection and monitoring of oil and gas pipelines.

The leak of hydrocarbon-carrying pipelines represents a serious incident, and if it is in a gas line, the economic exposure would be significant due to the high cost of lost or deferred hydrocarbon production. In addition, the leakage of hydrocarbon could pose risks to human life, have an impact on the environment, and could cause an image loss for the operating company. Pipelines are designed to operate at full capacity under steady-state flow conditions. Normal operations may involve day-to-day transients such as the operations of pumps, valves, and changes in production/delivery rates. The basic leak detection problem is to distinguish between the normal operational transients and the occurrence of non-typical process conditions that would indicate a leak. To date, the industry has concentrated on a single-phase flow, primarily of oil, gas, and ethylene. The application of a leak-monitoring system to a particular pipeline system depends on environmental issues, regulatory imperatives, loss prevention of the operating company, and safety policy rather than pipe size and configuration. This paper provides a review of the recommended guidance for leak detection of subsea pipelines in the context of pipeline integrity management. The paper also presents a review of the capability and application of various leak detection techniques that can be used to offer a roadmap to potential users of the leak detection systems.

The conventional oil pipeline leakage dynamic monitoring system has the problem of imperfect risk assessment link, which leads to the long alarm response time of the system. An oil and gas safety oriented oil pipeline leakage dynamic monitoring system is designed. Hardware part: select MLLD sensor as voltage acquisition device, and connect RS-485 serial standard bus and other circuit devices. Software part: identify the leakage hazard source of oil pipeline, capture the corresponding characteristic points of leakage negative pressure wave, determine the location of leakage point, build the risk assessment model based on oil and gas safety theory, design the safety management function of dynamic monitoring system software, and clarify the potential safety hazards of oil and gas pipeline. Experimental results: the average alarm response time of the oil pipeline leakage dynamic monitoring system based on data mining in this paper and the other two systems are 95.746s, 122.026s and 122.426s respectively, which proves that the oil pipeline leakage dynamic monitoring system integrating oil and gas safety theory has a higher value.

Summary Pipeline corrosion is an important issue that threatens the pipeline safety operation; therefore, the corrosion monitoring is essential. Based on the optical frequency domain reflectometry technology, which features distributed strain measurement with high resolution and high precision, a new method of pipeline internal corrosion monitoring is proposed to simultaneously locate corrosion and to evaluate corrosion severity. To verify the effectiveness and accuracy of this method, a series of tests were conducted including uniform corrosion test and local corrosion tests. These test results demonstrate that the corrosion location and corrosion severity evaluation can be achieved via the proposed distributed strain measurement, providing a valuable approach for pipeline corrosion monitoring.

Underwater blasting, often known as submarine blasting, is used for a wide range of purposes. This includes harbor and channel widening, trench excavation for establishing oil and gas pipelines and communication cables, demolition operations, and substructure construction. Particularly, underwater rock blasting is the most difficult and least understood source of vibration, which may have a significant impact on the safety of neighboring buildings and structures, especially berthing structures. The main aim of the study is to design the blasting patterns and monitor the blast vibrations on substructures during real blasting. Furthermore, it is designed to monitor vibration movements and manage them in order to protect the coastal environments from the blasting effects and ensure the safety of various building structures, as well as to maintain the blasting efficiency. Dredging occurs in deep water, with depths ranging from 16 to 20 m, to remove only around 5 m of rock. As a result of the aqueous layer above the rock, this sort of blasting action demands a higher level of competence and understanding of the related activities performed above the surface of the water. The measuring and monitoring of underwater blast-generated vibration in the coastline structures at Nhava Sheva Port, Navi Mumbai, Maharashtra, were discussed in this study. When using underwater explosives, proper safety precautions are taken to protect workers, other vessels in the blasting zone, and buildings from blasting vibrations. With a case study, the authors provide a thorough overview of their approach to underwater blasting utilizing existing blasting technologies.

Ensuring the structural integrity and operational safety of oil and gas pipelines is a critical challenge due to their extensive geographical coverage and exposure to environmental and anthropogenic risks. Traditional inspection approaches including ground patrols and manned aerial surveys are labor-intensive, costly, and often lack real-time responsiveness. While unmanned aerial vehicles (UAVs) enable flexible and high-resolution monitoring, their practical deployment requires lightweight, robust detection models capable of real-time inference on embedded edge hardware under heterogeneous environmental conditions. This paper presents an end-to-end, edge-deployable UAV inspection framework for simultaneous detection of above-ground pipelines and visually observable anomaly/leak indicators using the official Ultralytics YOLOv26n object detector. A curated dataset of 6127 UAV images acquired across desert, semi-urban, and industrial environments was annotated with two classes (Pipeline and Anomaly/Leak) and partitioned into training 87.5%, validation 8.3%, and testing 4.2% subsets. The detector was fine-tuned from COCO-pretrained weights for 300 epochs at 600 × 600 resolution and evaluated using COCO-style metrics. On the held-out test set, the proposed model achieved 92.4% mAP@0.5 and 75.0% mAP@0.5:0.95, with 89.7% precision, 90.2% recall, and 89.9% F1-score at the selected operating threshold. Optimized TensorRT deployment on an NVIDIA Jetson Xavier NX sustained real-time inference at 18 FPS, demonstrating suitability for onboard UAV processing. Rather than proposing a new detector architecture, the study contributes a domain-specific annotated UAV dataset, deployment-oriented benchmarking, and an end-to-end edge inference workflow for corridor-scale monitoring. The proposed framework can help reduce environmental contamination risk and improve personnel safety during pipeline inspection.

The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection ( Φ-OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multi-scene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial–temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and evaluation. Experiments have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%, and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy.