Distributed Sensor Research Articles

Parallel implementation of distributed acoustic sensor acquired signals: detection, processing, and classification

We aim to classify acoustic events recorded by a fiber optic distributed acoustic sensor (DAS). We derived the information from probing the fiber with light pulses and analyzing the Rayleigh backscatter. Then, we processed this data by a pipeline of processing algorithms to form the input for our machine learning classification model. We put random matrix theory to the test to distinguish the acoustic event of interest from the noise. We conditioned the raw trace using moving average and wavelet-based filtering algorithms to improve the signal-to-noise ratio. For raw, low pass, and wavelet-based filtered signals that we inject into a convolutional neural network, we rely on the magnitude of their complex coefficients to categorize the nature of the event. We also investigate Mel-Frequency Cepstral coefficients specific to the event as an input for the classifier and compare their performance to other signal representations. We run the experiments on the CNN for two-class and three-class classification using datasets from a DAS that is deployed for perimeter security and pipeline monitoring. We obtained the best results when using the MFCCs paired with wavelet denoising, achieving accuracies of 96.4% for the “event” class and 99.7% for the “no event” class when it comes to the two-class process. The three-class process yielded optimal accuracies of 83.3%, 81.3%, and 96.7% for the “digging,” “walking,” and “excavation” classes, respectively. Finally, the training execution time is exceptionally long because the extensive dataset and the model’s architecture are complex. As a result, we make efficient use of the CPU and GPU to maximize our machine’s power using the Keras API’s sequence data generator. Compared with the serial implementation, we report an improvement of up to 4.87 times.

Deep-Learning-Based Earthquake Detection for Fiber-Optic Distributed Acoustic Sensing

In this paper, deep learning models trained with real seismic data are proposed and proven to detect earthquakes in fiber-optic distributed acoustic sensor (DAS) measurements. The proposed neural network architectures cover the three classical deep learning paradigms: fully connected artificial neural networks (FC-ANNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Results demonstrate that training these networks with seismic waveforms measured by traditional broadband seismometers can extract and learn relevant features of earthquakes, enabling the reliable detection of seismic waves in DAS measurements. The intrinsic differences between DAS and seismograph waveforms, and eventual errors in the labelling of the DAS data, slightly reduce the performance of the models when tested with the distributed acoustic measurements. Despites of that, trained models can still reach up to 96.94% accuracy in the case of CNN and 93.86% in the case of CNN+RNN. The method and results here reported could represent an important contribution to the development of an early warning earthquake system based on DAS technology.

Multi-Source Separation Under Two “Blind” Conditions for Fiber-Optic Distributed Acoustic Sensor

Significant progress has been made in single source recognition for fiber-optical distributed acoustic sensor (DAS). However, it is still challenging to detect and identify more than one unpredictable vibration sources when they are superimposed at the same fiber receiving point. Thus, in this paper it is proposed a blind multi-source separation method based on fast independent component analysis (FastICA), which utilizes the independency and non-Gaussianity of different sources. Firstly, two multi-source mixing mechanisms and separability of different sources received by DAS based on Φ-OTDR are discussed; to solve the two “blind” problems that the source number and the mixing mode are both unknown, a linear simultaneous mixing mode is assumed, and the source number is estimated by singular value decomposition to the observation matrix; then preprocessing of denoising and anti-mixing, and separation with FastICA by maximizing negative entropy are carried out to make the non-Gaussianity of the estimated signal achieve its maximum; finally, feasibility of the separation method is evaluated through several mixing cases including simulations with two to four field collected signals and a real field test with two sources superimposed on the buried fiber. Signal waves and the spectra, and three separation indicators, such as the Performance Index (PI), the signal correlation coefficients, and the signal mean square error (SMSE), are used to evaluate the performance of the method. As far as we know, it is the first time to realize the separation of an unknown number of the superimposed sources detected by DAS.

Self-Assembled Highly Sensitive Hybrid Structure Sensor for Vector Curvature and Temperature Measurement

A highly sensitive hybrid structure sensor based on suspended-core fiber (SCF) is proposed for vector curvature and temperature measurement. The sensor is prepared by self-assembly of SCF-based cascaded Fabry-Perot interferometer (cFPI), single-mode fiber (SMF), and fiber Bragg grating (FBG). In view of the asymmetry of the cFPI, theoretical analysis of the beam transmission and spectral characteristics is implemented when the beam is incident from different directions. The two sensing fibers in the triangular distribution sensor will not be located on the neutral plane at the same time, which makes it possible to measure bending in any orientation. The Vernier effect formed by the cFPI enlarges the curvature and temperature sensitivity to −1064.5 pm/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> and 50.7 pm/°C, respectively, without affecting the measurement range. The spectrum shows the opposite spectral response when the sensor bending direction difference is 180°. The sensor overcomes the bottleneck of bending sensitive blind spots in dual-fiber sensing. In addition, the sensor maintains a smaller size and more flexible preparation, which is conducive to its application in artificial intelligence, medical detection.

Biomechanical Changes on the Typical Sites of Pressure Ulcers in the Process of Turning Over from Supine Position: Theoretical Analysis, Simulation, and Experiment.

Pressure ulcers are mainly caused by prolonged pressure on local tissues. The current method of preventing pressure ulcers is mainly to change the patient's position by turning, so it is significant to study the biomechanics of the typical site of pressure ulcers. Based on anatomical theory, a three-dimensional model of the shoulder and hip was established, and the theoretical contact pressure between the body and the bed was calculated by force analysis. Then, finite element models of typical parts of pressure ulcers were established, and the maximum stresses under different boundary conditions were obtained by finite element analysis. Finally, a human body turning experiment was conducted using a pressure distribution sensor, and the pressure distribution clouds and maximum contact pressure curves under different turning angles were obtained. The results show that the extreme point of maximum stress occurs at [Formula: see text], producing a stress concentration phenomenon; the peak stresses at the shoulder and hip are more balanced in the angular threshold range of [Formula: see text] to [Formula: see text], the stresses are more dispersed, and there exists an angular threshold for optimal integrated pressure, which can improve the efficiency of the use of assisted turning equipment. The relevant results help to explain the causes of pressure ulcer disease and can provide clinical references to improve the effectiveness of care.

Distributed Acoustic Sensor Using a Double Sagnac Interferometer Based on Wavelength Division Multiplexing

We demonstrated a fiber optic distributed acoustic sensor based on a double Sagnac interferometer, using two wavelengths separated by CWDM modules. A mathematical model of signal formation principle, based on a shift in two signals analysis, was described and substantiated mathematically. The dependence of the sensor sensitivity on a disturbance coordinate and frequency was found and simulated, and helped determine a low sensitivity zone length and provided sensor scheme optimization. A data processing algorithm without filtering, appropriate even in case of a high system noise level, was described. An experimental study of the distributed fiber optic sensor based on a Sagnac interferometer with two wavelengths divided countering loops was carried out. An accuracy of 24 m was achieved for 25.4 km SMF sensing fiber without phase unwrapping.

Pre- and Post-Rehabilitation Monitoring of a Steel Railway Bridge with Distributed and Discrete Sensors

Pre- and Post-Rehabilitation Monitoring of a Steel Railway Bridge with Distributed and Discrete Sensors

Flexible force-sensitive distributed pressure sensing system for soft connection of aircraft Cockpit Cover

With respect to the actual application of the edge bonding of transparent part of the fighter jet cockpit cover by soft connection, the flexible force-sensitive distributed pressure sensor system is developed, which covers the flexible force-sensitive sensor, calibration device, circuit and data analysis software. The pressure distribution during pressurizing of the edge bonding of multidimensional curved surface transparent parts is vividly showcased by 2D and 3D images. A number of people are assigned to carry out operation. Based on the pressure distribution map and data acquisition, it is determined that the existing bonding process has the serious decificiency of uneven pressure distributio that cannot be addressed, and there is huge potential for improvement.

Underground Vibration-Print Extraction and Spatial Attenuation Law Study of Ground-Borne Source Sensed by DAS

Complexity of underground environment brings great challenges to vibration source detection, recognition and localization for fiber-optic distributed acoustic sensor (DAS), which utilizes the buried optical communication cable as its sensing media. In order to explore the spatial attenuation laws of the ground-borne vibration propagated through the complicated underground media to the fiber, the theoretical and experimental studies are carried out for fundamental research and underground vibration-print based on the spatial power spectral distribution is proposed to represent the spatial spectral attenuation characteristics in both the axial and radial directions. Field tests with both the narrowband and broadband vibration sources have further proved that the proposed spatial power spectral distribution can reflect the distinguishability at different radial distances well and even the event types, which contributes to improve the perception ability such as accurate two-dimensional (2-D) localization and identification with DAS.

Fiber-Optic Sensors Evaluate Well Performance in Polymerflooding Pilot

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199023, “Evaluation of Well Performance Using Data From Fiber-Optic Sensors in Wells of a Pilot Polymerflooding Test, Orinoco Basin, Venezuela,” by Andres Ramirez, Edgar Vasquez, and Luis Quevedo, PDVSA, et al. The paper has not been peer reviewed. The complete paper describes a pilot test using polymerflooding in which evaluating the performance of injector wells was a main priority. The authors chose to complete the wells using a distributed temperature sensor. The paper describes how a better understanding of the connection between the well and the reservoir can improve reservoir simulation models, the decision-making process, and well-performance evaluation. The authors also stress the importance of determining the possibility of unswept areas in the reservoir, which could result in low volumetric sweep efficiency and, consequently, low recovery factor. Introduction The field is in the Orinoco Belt, mainly unconsolidated Miocene sandstone, and has produced heavy oil since 1999. At the time of writing, more than 711 producing wells have been drilled since the beginning of the exploitation of the field. After reaching a production plateau of 200,000 B/D, enhanced-oil-recovery technologies have been considered. One of these is polymerflooding. A pilot test was conducted in an area of the field in which conditions did not indicate application of any thermal recovery technique. The reservoir pressure at project startup was 315 psia, with a reservoir temperature of 115.5°F. Average porosity was 0.32, reservoir absolute permeability was estimated to be between 25 and 15 darcies, and reservoir thickness was 25 ft. Oil mobility was very low because of high viscosity. The pilot test began in 2016. Polymer at a concentration of 900 ppm was injected in a deltaic sedimentary environment composed of distributary channel fills and mouth bars. Polymer was injected through three injector wells. The objective of the test is to provide rapid answers to questions about injection performance, polymer stability, and the effect of heterogeneities. The results will determine if the technology represents a feasible opportunity for future development of the field. The objectives of the control plan in this phase include the following: - Establish how much of the horizontal section contributes to the well injectivity - Estimate an injection profile distribution - Determine if important changes occur during injection

Diagnosing multiphase flow regime in multilayered reservoir by distributed temperature sensor measurements

The article discusses the possibility of diagnosing the multiphase flow regime in the multilayered reservoir by using DTS (distributed temperature sensing) data. The analysis of the theoretical and actual curves of temperature build up and drawdown, corresponding to the main modes of multiphase flow of formation fluids are given in this article. The possibility of diagnosis of the multiphase flow regime is found based on analysis and interpretation of the characteristics of temperature build-up and drawdown in different intervals of the reservoir at the start of the well or shut in, or by changing opening degree of the choke. The possibility of diagnosing water breakthrough (WBT) intervals, based on the analysis of the changes in temperature curves according to the DTS is shown. For a more detailed analysis and interpretation of temperature change curves in the wells more frequent DTS measurements are required. It is necessary to conduct a comparative analysis of the dynamics of the temperature and pressure redistribution in the productive zone of the well, with the results of geophysical logging, production logging, taking samples of reservoir fluids from different zones of productive layers in the multilayered reservoir, moisture metering, hydrometry and others. Keywords: well; monitoring; multilayer reservoir; temperature profile; distributed temperature sensor; multiphase flow regime.

Hi/H∞-optimised fault detection for a surface vessel integrated navigation system

AbstractStrapdown inertial navigation systems are widely used in surface ships and warships. Although high-precision optical fibre inertial navigation systems are available, they have high cost and limited practicality. Therefore, they cannot replace the traditional platform inertial navigation systems in all ships. Hence, microelectromechanical system (MEMS)-based inertial sensors are widely used for robust navigation. Accurate and timely identification of sensor faults while ensuring stable navigation is a challenging task. This paper proposes a robust fault detection (FD) approach for a low-cost system that loosely integrates a strapdown inertial navigation system and the global navigation satellite system, where the integrated navigation state estimation provides high-accuracy output. A cubature Hi/H∞-optimised FD filter was designed for a nonlinear discrete time-varying system considering sensitivity to faults and robustness to disturbances. Furthermore, a threshold for FD was derived considering a compromise between the false alarm rate and fault diagnosis accuracy. Finally, the proposed method was validated through simulations using multiple noise distribution sensor data generated by a ship-manoeuvring simulator.

Experimental study of temperature change effect on distributed acoustic sensing continuous measurements

Distributed fiber-optic sensing is useful in geophysical exploration and monitoring applications. Distributed temperature sensing (DTS) is used for measuring and monitoring temperature and distributed acoustic sensing (DAS) for recording the seismic wavefield. However, DAS measurements also are sensitive to temperature changes. To understand and quantify the DAS signature of temperature changes during water injections at CO2CRC Otway site, a series of experiments have been conducted at the Curtin University/National Geosequestration Laboratory (NGL) well research facility and Curtin rock-physics laboratory. Overall, three DAS cables are examined. Two fibers are tested in the laboratory and one cable, which is installed behind the casing in the Curtin/NGL well, is examined in the well. Laboratory measurements and observations made during analysis of passive DAS and DTS field data recorded in four Otway wells demonstrate that DAS is sensitive to long-period temperature changes, and its response is proportional to the time derivative of temperature. Induced fiber strain is linearly related to slow temperature change, and this dependency can be estimated for a particular cable. Obtained proportionality constants between strain and temperature change indicate some dependency on the cable type/design and acquisition setup, but they are all of the same order of magnitude. DAS measurements also can be affected by low-frequency noise possibly associated with the effect of temperature on the DAS acquisition unit itself. The results can help compensate for the effect of temperature on low-frequency DAS signals and show that DAS can be used as a distributed temperature sensor if direct temperature measurements are not available.

The Namib Turbulence Experiment: Investigating Surface–Atmosphere Heat Transfer in Three Dimensions

Abstract The Namib Turbulence Experiment (NamTEX) was a multinational micrometeorological campaign conducted in the central Namib Desert to investigate three-dimensional surface layer turbulence and the spatiotemporal patterns of heat transfer between the subsurface, surface, and atmosphere. The Namib provides an ideal location for fundamental research that revisits some key assumptions in micrometeorology that are implicitly included in the parameterizations describing energy exchange in weather forecasting and climate models: homogenous flat surfaces, no vegetation, little moisture, and cloud-free skies create a strong and consistent diurnal forcing, resulting in a wide range of atmospheric stabilities. A novel combination of instruments was used to simultaneously measure variables and processes relevant to heat transfer: a 3-km fiber-optic distributed temperature sensor (DTS) was suspended in a pseudo-three-dimensional array within a 300 m × 300 m domain to provide vertical cross sections of air temperature fluctuations. Aerial and ground-based thermal imagers recorded high-resolution surface temperature fluctuations within the domain and revealed the spatial thermal imprint of atmospheric structures responsible for heat exchange. High-resolution soil temperature and moisture profiles together with heat flux plates provided information on near-surface soil dynamics. Turbulent heat exchange was measured with a vertical array of five eddy-covariance point measurements on a 21-m mast, as well as by collocated small- and large-aperture scintillometers. This contribution first details the scientific goals and experimental setup of the NamTEX campaign. Then, using a typical day, we demonstrate (i) the coupling of surface layer, surface, and soil temperatures using high-frequency temperature measurements, (ii) differences in spatial and temporal standard deviations of the horizontal temperature field using spatially distributed measurements, and (iii) horizontal anisotropy of the turbulent temperature field.

Planning and Operations of the Hydrate 01 Stratigraphic Test Well, Prudhoe Bay Unit, Alaska North Slope

The National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey are leading an effort to conduct an extended gas hydrate production test in northern Alaska. The proposed production test required the drilling of an initial stratigraphic test well (STW) to confirm the geologic conditions of the proposed test site. This well was completed in January 2019 in cooperation with the Prudhoe Bay Unit Working Interest Owners. The Prudhoe Bay Unit Hydrate-01 STW was spudded on 10-December-2018. Downhole data acquisition was completed on 25-December-2018, and the rig was released on 01-January-2019. The Hydrate-01 STW was drilled in two sections, including the surface hole that was drilled to a depth of 2248 ft measured depth (MD) (685 m MD) and cased, and the production hole section that was drilled to a depth of 3558 ft MD (1084 m MD) and also cased. A thermally chilled mineral-oil-based mud was used in the main (production) hole section of the well to maintain wellbore stability and quality of the wellbore acquired data. The primary wellbore data were acquired using logging-while-drilling tools. A sidewall pressure core system was also deployed to gather grain size and other data needed for the design of the future production test wells. In addition to confirming the geologic conditions at the test site, the Hydrate-01 STW was designed to serve as a monitoring well during future field operations. Therefore, two sets of fiber-optic cables, each including a bundled distributed acoustic sensor (DAS) and a distributed temperature sensor (DTS), were clamped to the outside of the production casing and cemented in place. In March 2019, the project team acquired three-dimensional (3D) DAS vertical seismic profiling data in the Hydrate-01 STW. Temperature surveys were also acquired with the DTS as deployed in the Hydrate-01 STW during the completion of the well and nearly continuously since March-2019.

Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data

The security and energy efficiency of resource-constrained distributed sensors are the major concerns in the Internet of Things (IoT) network. A novel lightweight compressive sensing (CS) method is proposed in this study for simultaneous compression and encryption of sensor data in IoT scenarios. The proposed method reduces the storage space and transmission cost and increases the IoT security, with joint compression and encryption of data by image sensors. In this proposed method, the cryptographic advantage of CS with a structurally random matrix (SRM) is considered. Block compressive sensing (BCS) with an SRM-based measurement matrix is performed to generate the compressed and primary encrypted data. To enhance security, a stream cipher-based pseudo-error vector is added to corrupt the compressed data, preventing the leakage of statistical information. The experimental results and comparative analyses show that the proposed scheme outperforms the conventional and state-of-art schemes in terms of reconstruction performance and encryption efficiency.

Melt coated flexible stainless-steel thread based co-axial triboelectric yarn nanogenerators

ABSTRACT Textiles that are smart and responsive have become the new norm in recent years. The development of smart textiles has sparked research into self-powered distributed sensors and portable wearable electronics integrated into clothing. One of the main functionalities of smart textiles is the ability to power these electronics by generating electrical energy from body movements. Since fibres, yarns, and filaments are the basic units of textiles, integrating smart functionality into these basic units is a rational approach. Herein, we report on a co-axial yarn-like triboelectric nanogenerator (Y-TENG) by utilising a scaled-up dielectric (i.e. PVDF) melt coating method to coat a flexible stainless steel thread. The average peak open-circuit voltage (Voc), peak Voc, average peak short-circuit current (Isc), and peak Isc were recorded as 3.82 V, 16.1 V, 12.96 nA, and 53.1 nA, respectively, at 700 N force and 2.11 Hz frequency.

Markov Transition Fields and Deep Learning-Based Event-Classification and Vibration-Frequency Measurement for φ-OTDR

In this paper, a novel method, relying on Markov Transition Fields (MTF) and deep learning, is proposed to classify the vibration-events and measure vibration-frequency, for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {\varphi } $ </tex-math></inline-formula> -OTDR based fiber-optic distributed vibration sensor. The normalized time series of a signal detected by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varphi }$ </tex-math></inline-formula> -OTDR are converted into an image of MTF, serving as a sample for supervised learning. Next, the MTF image is classified using a convolutional neural network (CNN) and a fully connected neural network. Initially, five different vibration-events including blowing, rain, direct and indirect knocking, and false vibration caused by noises, are classified. Furthermore, approximate single-frequency vibrations (with different central-frequency) are regarded as different classifications, where vibration frequency can be obtained by classification tasks. Compared to conventional method based on phase-demodulation with complex techniques and high cost, the proposed method demonstrates a low-cost and fast-time method. Moreover, learning algorithm is trained and tested through the data sets generated by experiments. To evaluate feasibility and validate the performance of classification, we analyze the test accuracy of classification and relevant receiver operating characteristic curves. The results indicate that our method is effective for the classification of both vibration-events and single-frequency vibrations. We believe that this work provides not only a technical reference for the application of deep learning in measuring the vibration frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varphi } $ </tex-math></inline-formula> -OTDR using software-technique, but also an impressive inspiration for event-classification of vibrations by a combination of image-processing methods.

A Low-Power Distributed Visual Sensor Network for Real-Time Barcode Localization and Identification.

A novel low-power distributed Visual Sensor Network (VSN) system is proposed, which performs real-time collaborative barcode localization, tracking, and robust identification. Due to a dynamic triggering mechanism and efficient transmission protocols, communication is organized amongst the nodes themselves rather than being orchestrated by a single sink node, achieving lower congestion and significantly reducing the vulnerability of the overall system. Specifically, early detection of the moving barcode is achieved through a dynamic triggering mechanism. A hierarchical transmission protocol is designed, within which different communication protocols are used, depending on the type of data exchanged among nodes. Real-Time Transport Protocol (RTP) is employed for video communication, while the Transmission Control Protocol (TCP) and Long Range (LoRa) protocol are used for passing messages amongst the nodes in the VSN. Through an extensive experimental evaluation, we demonstrate that the proposed distributed VSN brings substantial advantages in terms of accuracy, power savings, and time complexity compared to an equivalent system performing centralized processing.

High-sensitivity dynamic distributed pressure sensing with frequency-scanning φ-OTDR.

We propose a high-sensitivity dynamic distributed pressure sensor using frequency-scanning phase-sensitive optical time-domain reflectometry (φ-OTDR) in a single-mode fiber (SMF), where an injection locking laser working as both filter and amplifier is used to generate the multi-frequency signals under a double-sideband modulation. The pressure sensitivity of the SMF is measured to be 702.5 MHz/MPa, which is approximately 1000 times larger than that of the Brillouin optical time-domain analysis technique. Subsequently, a dynamic pressure experiment is carried out in the case of rapid pressure relief from 2 to 0 MPa so that a maximum sampling rate of 33.3 kHz for a 25-m SMF is achieved, and the measurement uncertainty of 0.61 kPa for the proposed scheme is demonstrated.