Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Design overview of high pressure dense phase CO2 pipeline transport in flow mode

In open literature, there is little information available with regards to the engineering and technological issues for material corrosion, in relation to high pressure supercritical CO2 pipeline transport from single point sources, such as the power industry. A typical CO2 pipeline is designed to operate at high pressure in the dense phase. However, it is evident that although there is considerable experience of testing materials in lower pressure gaseous CO2 in the oil and gas industry, there is little understanding of the behaviour of pipeline materials when in contact with impure CO2 captured either from power plants or the oil and gas industry. In this particular project development, a dynamic dense phase CO2 corrosion rig has been built (conditions: ∼85 bar, 40 °C and up to 5 l/min flow rate) in flow mode, to understand the effect of impurities (SO2, O2, H2, NO2 & CO) present in captured CO2 on the pipeline transport materials. This unique facility in the UK was developed via the MATTRANS project funded by the E.ON-EPSRC strategic partnership (EP/G061955/1). The test rig includes different metallic materials (X grade steel: X60, X70 and X100) to assess the corrosion of pipelines, and different geometry components (tubes, plates, charpy and tensile coupons), to assess ageing and decompression behavior of polymeric seals (Neoprene, fluorocarbon, ethylene and Buna N) under water-saturated dense phase CO2 with different impurity concentrations (0.05 mol % SO2; 4 mol % O2; 2 mol % H2; 0.05 mol % NO2; 1 mol % CO). The dynamic data generated from this dense phase CO2 corrosion rig will give vital information with regards to pipeline suitability and lifetimes, when operating with dense CO2

Research progress on coal mine laser methane sensor

This paper discusses the research progress of low-power technology of laser methane sensors for coal mine. On the basis of environment of coal mines, such as ultra-long-distance transmission and high stability, a series of studies have been carried out. The preliminary results have been achieved in the research of low power consumption, temperature and pressure compensation and reliability design. The technology is applied to various products in coal mines, and achieves high stability and high reliability in products such as laser methane sensor, laser methane detection alarm device, wireless laser methane detection alarm device, and optic fiber multichannel laser methane sensor. Experimental testing and analysis of the characteristics of laser methane sensors, combined with the actual application

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Review: optical fiber sensors for civil engineering applications

Optical fiber sensor (OFS) technologies have developed rapidly over the last few decades, and various types of OFS have found practical applications in the field of civil engineering. In this paper, which is resulting from the work of the RILEM technical committee “Optical fiber sensors for civil engineering applications”, different kinds of sensing techniques, including change of light intensity, interferometry, fiber Bragg grating, adsorption measurement and distributed sensing, are briefly reviewed to introduce the basic sensing principles. Then, the applications of OFS in highway structures, building structures, geotechnical structures, pipelines as well as cables monitoring are described, with focus on sensor design, installation technique and sensor performance. It is believed that the State-of-the-Art review is helpful to engineers considering the use of OFS in their projects, and can facilitate the wider application of OFS technologies in construction industry

Measured Water Temperature Characteristics in a Pipeline Distribution System

YesThis paper describes the design, development, deployment and performance assessment of a prototype system for monitoring the 'health' of a water distribution network based on the temperature distribution and time-dependent variations in temperature across the network. It has been found that the water temperature can reveal unusual events in a water distribution network, indicated by dynamic variations in spatial temperature differential. Based on this indication it is shown how patterns of changes in the water temperature can be analysed using AQUIS pipeline distribution software and used in conjunction with hydraulic (e.g. flow and pressure) sensors to indicate the state of ¿health¿ of the network during operation

Unsupervised Parkinson’s Disease Assessment

Parkinson’s Disease (PD) is a progressive neurological disease that affects 6.2 million people worldwide. The most popular clinical method to measure PD tremor severity is a standardized test called the Unified Parkinson’s Disease Rating Scale (UPDRS), which is performed subjectively by a medical professional. Due to infrequent checkups and human error introduced into the process, treatment is not optimally adjusted for PD patients. According to a recent review there are two devices recommended to objectively quantify PD symptom severity. Both devices record a patient’s tremors using inertial measurement units (IMUs). One is not currently available for over the counter purchases, as they are currently undergoing clinical trials. It has also been used in studies to evaluate to UPDRS scoring in home environments using an Android application to drive the tests. The other is an accessible product used by researchers to design home monitoring systems for PD tremors at home. Unfortunately, this product includes only the sensor and requires technical expertise and resources to set up the system. In this paper, we propose a low-cost and energy-efficient hybrid system that monitors a patient’s daily actions to quantify hand and finger tremors based on relevant UPDRS tests using IMUs and surface Electromyography (sEMG). This device can operate in a home or hospital environment and reduces the cost of evaluating UPDRS scores from both patient and the clinician’s perspectives. The system consists of a wearable device that collects data and wirelessly communicates with a local server that performs data analysis. The system does not require any choreographed actions so that there is no need for the user to follow any unwieldy peripheral. In order to avoid frequent battery replacement, we employ a very low-power wireless technology and optimize the software for energy efficiency. Each collected signal is filtered for motion classification, where the system determines what analysis methods best fit with each period of signals. The corresponding UPDRS algorithms are then used to analyze the signals and give a score to the patient. We explore six different machine learning algorithms to classify a patient’s actions into appropriate UPDRS tests. To verify the platform’s usability, we conducted several tests. We measured the accuracy of our main sensors by comparing them with a medically approved industry device. The our device and the industry device show similarities in measurements with errors acceptable for the large difference in cost. We tested the lifetime of the device to be 15.16 hours minimum assuming the device is constantly on. Our filters work reliably, demonstrating a high level of similarity to the expected data. Finally, the device is run through and end-to-end sequence, where we demonstrate that the platform can collect data and produce a score estimate for the medical professionals

Application and research of wireless laser methane sensor in drainage pipeline monitoring

Laser methane sensor has been widely promoted and successfully applied in coal mines as a new and effective technology building on the approach of laser-based absorption detection. Compared with the traditional catalytic methane sensor, the laser methane sensor discussed offers the important advantages of a long calibration period, high detection precision, the absence of zero drift and low power consumption, all of which are significant advantages for use in coal mining applications. By compensating for the temperature and pressure of the gases present, the accuracy of the methane sensor is evident across a wide range of temperatures and pressures, making it suitable for gas detection, including methane, in pipelines as well. The wireless laser approach which is incorporated into the methane sensor allows wireless transmission and data uploading to a cloud server through NB-IoT. This tackles the problem in gas pipeline monitoring of the length of many pipelines and thus the wide distribution of the sensors, avoiding complicated wiring and thus high associated cost. Further, remote data management can then be achieved, all of which greatly improves the flexibility and security of the management of the pipeline and the data generated