Time Domain Reflectometry Sensors Research Articles

An Innovative Time Domain Reflectometry (TDR) Sensor to Monitor Earthquake Induced Void Redistribution in Layered Soils

An Innovative Time Domain Reflectometry (TDR) Sensor to Monitor Earthquake Induced Void Redistribution in Layered Soils

Assessing Effect of Irrigation Frequency on Evaporation and Transpiration in Vineyards Using SIMDualKc Simulation Model with Measured Wet Fraction

Irrigation efficiency is important for the environment and the economy. SIMDualKc has been effectively used to calculate evaporation and transpiration separately in vineyards for different irrigation strategies, but not to analyze the impact of irrigation frequency. This study was conducted for the in-field adjustment of the soil wetted fraction (wf) with a time domain reflectometry (TDR) sensor as a function of different irrigation frequency treatments (T03, T07, and T15 with irrigation every 3, 7, and 15 days, respectively). Evaporation and transpiration values were estimated separately with the SIMDualKc model for different irrigation frequencies with the adjusted wf, comparing them with vineyard field measurements and analyzing the effect of different irrigation frequencies on vineyard yield. The wf in T15 was higher than that in T07, which in turn was higher than that in T03. SIMDualKc indicates the most unfavorable treatment to be the T07, with higher evaporation and lower transpiration than T15 and T03, while T15 had higher evaporation and lower transpiration than T03. Although stomatic conductance also indicates the most unfavorable case to be T07, no clear differences were found between T03 and T15. No statistically significant differences were obtained in yield. The results indicate that the differences observed between irrigation frequencies are not reflected in yield but do show up in water use efficiency.

Characterization of cement–slime mixture using time domain reflectometry

This study explores the electromagnetic characteristics of five cement–slime mixtures using two types of time domain reflectometry sensors. For 28 days of curing, compressive strength tests are conducted and electromagnetic signals are measured. The electromagnetic wave velocity and corresponding apparent permittivity are calculated, and the relationships between these properties and the compressive strengths of the mixtures are established. Results show that in the initial curing period, capturing the apparent permittivity using a conventional probe proves difficult due to the high electrical conductivity of the mixtures. In contrast, an insulated electrical wire can detect reflected signals across all slime ratios, but it exhibits less sensitivity to changes in the electromagnetic signal. The apparent permittivity decreases exponentially over the curing time, influenced by the hydration process. Strong correlations are found between the apparent permittivities derived from both sensors and between decreasing apparent permittivity and increasing compressive strength.

Estimation of buried pipe depth in an artificial soil tank using ground-penetrating radar and moisture sensor

A method is required for accurate determination of the location (horizontal position and depth) of subsurface utilities with minimal digging. Ground-penetrating radars (GPRs) have been used at many sites as a nondestructive method for detecting underground structures. Previous studies have examined the accuracy and resolution of GPRs based on comparisons of exploration images and factors such as soil, pipe material, and environmental conditions. Although numerous studies have explored the influence of relative permittivity and water content on GPR surveys, only a limited number have conducted direct measurements of water content using a moisture sensor within a soil tank and applied this data to enhance the precision of depth estimation. In this study, the effects of pipe material, relative permittivity, and antenna frequency on the depth estimation accuracy were quantitatively investigated using an artificial soil tank made of silica sand with known physical properties. In addition to the GPR survey, relative permittivity measurements were conducted using time-domain reflectometry (TDR) sensors located at different water levels. The results show that the antenna frequency and pipe material affect the resolution but not the depth estimation accuracy. Furthermore, the GPR survey using the relative permittivity from the TDR sensors estimated the pipe depths with a relative error of <7.33% relative to the actual burial depth under the following conditions: a volumetric water content of 2.6–38.2%, the relative permittivity of 2.9–23.2, and buried pipes of 0.1 m in diameter and 1.5 m in depth. The study findings show that the selection of an appropriate frequency and the estimation of soil permittivity using a moisture sensor can improve the depth estimation accuracy of buried pipes by GPR survey.

Assessing Low-cost Capacitive Soil Moisture Sensors: Accurate, Affordable, and IoT-ready Solutions for Soil Moisture Monitoring

This research, conducted at G D Goenka University, assesses the performance of low-cost capacitive soil moisture sensors in comparison to the standard Time Domain Reflectometry (TDR) sensors, with a focus on accuracy, reliability, cost-effectiveness, and IoT integration. The results indicate that these sensors are highly accurate, exhibit low error rates, and are a cost-effective alternative to TDR sensors, making them well-suited for large-scale deployments in agricultural and environmental applications. The study's findings support the integration of these sensors into IoT-based soil moisture monitoring systems, with recommendations for optimizing their performance in specific use cases, thereby contributing valuable insights to the field of soil moisture measurement.

Improving the Calibration of Surface Time Domain Reflectometry Sensors for Moisture Evaluation of Building Materials Using the Analysis of Covariance Method

Improving the Calibration of Surface Time Domain Reflectometry Sensors for Moisture Evaluation of Building Materials Using the Analysis of Covariance Method

Radio Frequency-Based Diagnostics for Superconducting Magnets

Operation of high-field superconducting magnets relies on diagnostic instrumentation for measuring strain, temperature, and magnetic field variations, detecting quenching, and identifying performance problems. Discrete sensor implementation is costly, requires multi-channel data acquisition systems, and sensor density is often insufficient to resolve problematic locations spatially. Distributed sensing is a viable alternative approach providing location-specific diagnostic information over single terminal output. In particular, it can be the key to quench protection of high-temperature superconductor (HTS)-based magnets where hot spots are known to form and persist before a quench. Fiber-optic technology offers distributed sensing solution for magnets but suffers from drawbacks such as fiber fragility, high costs of optical interrogators, and difficulty in differentiating between physical quantities such as temperature and strain. We propose an alternative, robust, and easily integrable way of implementing distributed sensing in magnets using radio-frequency (RF) technologies. RF Time Domain Reflectometry (TDR) technique has been around for nearly 60 years, and it is an essential diagnostic tool used in multiple areas of technology and applied research. We discuss operational principles and practical implementation of RF TDR sensors capable of detecting local variations of strain, temperature, and magnetic field through changes in RF impedance and wave propagation time. Results of cryogenic testing of our TDR sensors with HTS tape conductors are presented. The perspective of enabling a new diagnostics paradigm for high-energy physics and fusion energy applications based on distributed RF sensing is discussed.

The Feasibility of Remotely Sensed Near-Infrared Reflectance for Soil Moisture Estimation for Agricultural Water Management

In-depth knowledge about soil moisture dynamics is crucial for irrigation management in precision agriculture. This study evaluates the feasibility of high spatial resolution near-infrared remote sensing with unmanned aerial systems for soil moisture estimation to provide decision support for precision irrigation management. A new trapezoid model based on near-infrared transformed reflectance (NTR) and the normalized difference vegetation index (NDVI) is introduced and used for estimation and mapping of root zone soil moisture and plant extractable water. The performance of the proposed approach was evaluated via comparison with ground soil moisture measurements with advanced time domain reflectometry sensors. We found the estimates based on the NTR−NDVI trapezoid model to be highly correlated with the ground soil moisture measurements. We believe that the presented approach shows great potential for farm-scale precision irrigation management but acknowledge that more research for different cropping systems, soil textures, and climatic conditions is needed to make the presented approach viable for the application by crop producers.

Compensation of Phase Noise Impairments in Distributed Acoustic Sensors Based on Optical Pulse Compression Time-Domain Reflectometry

We introduce a method to compensate for the deleterious effects of the phase noise of the laser source on long-range distributed acoustic sensors (DAS) that implement optical pulse compression (OPC). Pulse compression can be used in coherent optical time-domain reflectometry (COTDR) sensors to extend the measurement range without compromising spatial resolution. In fact, OPC-COTDR sensors have demonstrated the longest measurement range to date in passive sensing links that do not require distributed amplification to compensate fiber attenuation. However, it has been found that the limited coherence of the laser source has a degrading effect on the actual performance enhancement that pulse compression can bring because it constrains the maximum duration of the compression waveforms that can be used and makes the use of lasers with extremely low phase noise necessary. We introduce a technique to compensate for the effects of phase noise on OPC-COTDR sensors so that they can demonstrate their full potential for long-range measurements using lasers with less stringent phase noise requirements. The method is based on sampling the phase noise of the laser with an auxiliary interferometer and using this information in a simple signal processing technique to mitigate its deleterious effect on the signal measured. We test our method in an OPC-COTDR sensor that uses 500- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s linear frequency modulated pulses to demonstrate 100-km range measurements with 200 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm{p}\epsilon /\sqrt{\rm{Hz}}$</tex-math></inline-formula> of strain sensitivity at 2-m initial spatial resolution that becomes 10-m after applying the gauge length. To our knowledge, this is the longest compression waveform demonstrated to date in an OPC-COTDR sensor. Its use provides an extra 20-km range compared to previous demonstrations using laser sources of comparable linewidth. Furthermore, comparable performance is also demonstrated when using a laser source with an order of magnitude larger linewidth.

Fast and Accurate Brillouin Optical Time-Domain Sensing by Sparse Frequency Sampling and ANN-Based Recover Method

We propose a novel sparse frequency sampling and Brillouin scattering spectrum (BSS) recover method to greatly reduce the acquisition time of the frequency-sweep based Brillouin optical time domain sensor (BOTDS) without increasing hardware complexity. In the proposed method, the acquired BSSs only contain a few frequency points and thus show low resolution due to the sparse frequency sampling strategy. Then an artificial neural network (ANN) is used to accurately recover the BSSs with sparse frequency sampling to that with normal frequency sampling, and thus achieve fast and accurate temperature/strain measurement. In a proof-of-concept experiment, we measured the BSS of a 3 km sensing fiber and achieved 3 m spatial resolution by using a Brillouin optical time domain reflectometry (BOTDR) sensor. Through sparse frequency sampling, the data acquisition time is 5.5% with respect to that of the normal sampling. When the average (AVG) times of BSS is higher than 1000, the temperature measurement uncertainty is 0.2 °C.

Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks

AbstractInterpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution.

Design a remote sensing of multi-BOTDR fiber optic sensors for fuel pipeline monitoring

The necessity for environmental protection has led to the development of different sensors for monitoring fuel pipelines to avoid explosion, leakage, and corrosion. Distributed optical fiber sensors-based Brillouin scattering have become progressively common because of their novel feature of simultaneously determining temperature, vibration, and strain. The stimulated Brillouin scattering (SBS) threshold power, sensing distance, single-mode fiber cable length, and sensing accuracy are serious parameters to the implementation of Brillouin optical time-domain reflectometry (BOTDR) sensor. In this study, remote sensing of multi-BOTDR fiber optic sensors for fuel pipeline monitoring is designed and presented. The results of simulations and theoretical explainations on suppressing fiber optic nonlinearity are analyzed. Results show that an SBS suppression via 16 dB is required to attain the maximum multi-remote sensing distance of up to 20 km for each network and a perfect frequency shift of 11 GHz. An open eye pattern corresponds to minimal signal distortion with a high Q-factor of 22 for the developed sensor. The highest sensing accuracy of 0.3318 %/K indicating power change (%) is observed from 290–300 K at 193.1 THz operating frequency.

Lamb Wave Detection for Structural Health Monitoring Using a ϕ-OTDR System.

In this paper, the use of a phase-sensitive optical time-domain reflectometry (ϕ-OTDR) sensor for the detection of the Lamb waves excited by a piezoelectric transducer in an aluminum plate, is investigated. The system is shown to detect and resolve the Lamb wave in distinct regions of the plate, opening the possibility of realizing structural health monitoring (SHM) and damage detection using a single optical fiber attached to the structure. The system also reveals the variations in the Lamb wave resulting from a change in the load conditions of the plate. The same optical fiber used to detect the Lamb waves has also been employed to realize distributed strain measurements using a Brillouin scattering system. The method can be potentially used to replace conventional SHM sensors such as strain gauges and PZT transducers, with the advantage of offering several sensing points using a single fiber.

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.

A Study on Peak Determinations by Analyzing Spectral Intensity Trends for Brillouin Optical Time Domain Reflectometry

Brillouin optical time domain reflectometry sensor utilizes a peak frequency in a Brillouin gain spectrum to measure distributed strain and/or temperature. When the strain and/or temperature changes significantly within a spatial resolution at the measuring position, the measured Brillouin gain spectrum has multi‐peaks with competing intensities and the noise overlapping may cause a big jump to a falsely determined frequency. Such a rare spot has been overlooked but are found by recent sensors with a higher sampling resolution. The signal‐to‐noise ratio improvement or multi‐peak fitting methods is effective, but it cannot eliminate this problem in essence. In this study, an analytical method is developed to correct the erroneous peak determinations based on the nature of the spectral‐intensity trends along with the distance axis. When a peak frequency transits from one peak to the other, the intensity of the former peak decreases and that of the later increases, and those levels intersect. Simulations are conducted with a spatial resolution of 1 m for frequency transitions with a frequency difference of 3–300 MHz. For differences larger than an intrinsic Brillouin bandwidth, this method is effective in avoiding a large jump to a false peak. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Comparison of the Moist Material Relative Permittivity Readouts Using the Non-Invasive Reflectometric Sensors and Microwave Antenna.

The article concerns the issue of non-invasive moisture sensing in building materials. Two techniques that enable evaluating the value of the relative permittivity of the material, being the measure of porous material moisture, have been utilized for the research. The first is the microwave technique that utilizes the non-contact measurement of velocity of microwave radiation across the tested material and the second is the time domain reflectometry (TDR) technique based on the measurement of electromagnetic pulse propagation time along the waveguides, being the elements of sensor design. The tested building material involved samples of red ceramic brick that differed in moisture, ranging between 0% and 14% moisture by weight. The main goal of the research was to present the measuring potential of both techniques for moisture evaluation as well as emphasize the advantages and disadvantages of each method. Within the research, it was stated that both methods provide similar measuring potential, with a slight advantage in favor of a microwave non-contact sensor over surface TDR sensor designs.

Phase Noise Effects on Phase-Sensitive OTDR Sensors Using Optical Pulse Compression

We introduce a detailed theoretical, numerical, and experimental study of the effects of laser’s phase noise on the performance of phase-sensitive optical time-domain reflectometry ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> -OTDR) sensors that use optical pulse compression (OPC). Pulse compression is a technique that can be used to improve the received signal amplitude by increasing the effective energy of the pulses that are launched into the fiber without degrading the spatial resolution of the measurements. Therefore, it is a valuable tool to extend the range of these sensors and mitigate fiber attenuation constraints. However, it has been observed that the limited coherence of the laser source has a degrading effect on the actual performance enhancement that this method can provide. Here, we derive a theoretical model that can be used to quantify this degradation for any type of OPC such as those based on either linear frequency modulation (LFM) pulses or perfect periodic autocorrelation (PPA) bipolar bit sequences. The model facilitates numerical estimation of the sensitivity of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> -OTDR measurements. It also produces theoretical expressions for the mean and the variance of the phase-noise perturbed backscatter response. These results are validated via numerical simulations and experiments in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> -OTDR setups using LFM as well as PPA OPC. Furthermore, we demonstrate the use of the model to investigate the basic trade-offs involved in the design of OPC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> -OTDR systems.

Quantitation of Water Addition in Octopus Using Time Domain Reflectometry (TDR): Development of a Rapid and Non-Destructive Food Analysis Method.

A rapid and non-destructive method based in time domain reflectometry analysis (TDR), which detects and quantifies the water content in the muscle, was developed for the control of abusive water addition to octopus. Common octopus samples were immersed in freshwater for different periods (0.5–32 h) to give a wide range of moisture contents, representing different commercial conditions. Control and water-added octopus were analyzed with a TDR sensor, and data correlated with moisture content were used for calibration and method validation. A maximum limit of moisture content of 85.2 g/100 g in octopus is proposed for conformity assessment, unless the label indicates that water (>5%) was added. Calibration results showed that TDR analysis can discriminate control and water-added octopus, especially for octopus immersed for longer periods (32 h). In addition, moisture content can be quantified in octopus using only TDR analysis (between 80 and 90 g/100 g; RMSE = 1.1%). TDR data and correlation with moisture content show that this non-destructive methodology can be used by the industry and quality control inspections for assessment of octopus quality and to verify compliance with legislation, promoting fair trade practices, and further contributing to a sustainable use of resources.

Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing

High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model’s algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps.

Determination of Time Domain Reflectometry Surface Sensors Sensitivity Depending on Geometry and Material Moisture.

The article concerns the electric techniques of moisture detection that are based on the evaluation of the apparent permittivity of the tested medium. The main goal of the research was to evaluate the non-invasive Time Domain Reflectometry (TDR) sensors’ sensitivity by measuring the span of elements and material moisture. To that aim, two non-invasive sensor designs were investigated for their sensitivity in the evaluation of the apparent permittivity value of aerated concrete. Sensors A and B were characterized by the spacing between the measuring elements equal to 30 mm and 70 mm, respectively. The tested samples differed in moisture, ranging between 0 and 0.3 cm3/cm3 volumetric water content. Within the research, it was stated that in the case of the narrower sensor (A), the range of the sensor equals about 30 mm, and in the case of the wider design (B), it equals about 50 mm. Additionally, it was stated that material moisture influences the range of sensor influence. In the case of the dry and low-saturated material, it was not possible to evaluate the range of sensor sensitivity using the adopted method, whereas the range of sensor signal influence was visible for the moist material.