Time Domain Reflectometry Response Research Articles

A permittivity-conductivity joint model for hydrate saturation quantification in clayey sediments based on measurements of time domain reflectometry

Hydrate saturation (Sh) is one of the key parameters for resource assessment of hydrate reservoirs and production optimization of natural gas. There are still significant challenges in determining the Sh in clayey formations. Both dielectric and resistivity logging tools have been used for identifying and evaluating hydrate-bearing formations; however, there is little work on a joint analysis and modelling of the permittivity and resistivity for quantifying the Sh. To bridge the knowledge gap, we have proposed a novel permittivity-conductivity (P–C) joint approach based on TDR (time domain reflectometry)-derived parameters (i.e., apparent permittivity Ka and bulk conductivity σdc) in this work. The proposed P–C joint approach can provide a theoretical basis for the joint interpretation of dielectric and resistivity geophysical measurements on hydrate-bearing formations in the field. First, the basic theory for deriving the Ka and σdc from the TDR responses of hydrate-bearing sediments was formulated based on the dielectric polarization and electrical conduction mechanisms. Second, an experimental campaign was carried out including the development of experimental system, calibration of TDR probe and design of experimental scheme. Third, the influences of hydrate saturation, clay mineralogy and clay content on the TDR responses of unconsolidated sediments were examined. Then the Ka and σdc were related to Sh respectively, and finally a novel P–C joint model for the quantification of Sh in clayey sediments was established and verified. It has been demonstrated that: (1) the Ka of the clayey samples with hydrates decreases almost linearly with an increasing clay content up to 20 %, while the σdc of the smectite-bearing samples decreases nonlinearly in contrast to the linear trend for illite; (2) the power-law mixing formula incorporating an empirical exponent is a preferable permittivity model for hydrate-bearing clayey sediments due to its merits of empirical and theoretical nature, while the Simandoux equation is effective to account for the clay effects on the conductivity of hydrate-bearing sediments with smectite and illite; (3) the P–C joint model can be established by utilizing the porosity of hydrate-bearing sediments as a bridge parameter between Ka and σdc. The variation behavior of Ka and σdc with different types and contents of clay minerals can be explained by the difference of the amount of bound water and swelling effects between the illite-bearing and smectite-bearing samples. The proposed P–C joint model outperforms the standalone permittivity-based and conductivity-based models especially for the clayey cases. The root-mean-squared errors of the P–C joint models are 7.339, 2.930 and 2.065 % for the clean-sand samples, clayey samples with illite and smectite, respectively.

Novel Approach to Phase-Sensitive Optical Time-Domain Reflectometry Response Analysis with Machine Learning Methods.

This paper is dedicated to the investigation of the metrological properties of phase-sensitive reflectometric measurement systems, with a particular focus on addressing the non-uniformity of responses along optical fibers. The authors highlight challenges associated with the stochastic distribution of Rayleigh reflectors in fiber optic systems and propose a methodology for assessing response non-uniformity using both cross-correlation algorithms and machine learning approaches, using chirped-reflectometry as an example. The experimental process involves simulating deformation impact by altering the light source's wavelength and utilizing a chirped-reflectometer to estimate response non-uniformity. This paper also includes a comparison of results obtained from cross-correlation and neural network-based algorithms, revealing that the latter offers more than 34% improvement in accuracy when measuring phase differences. In conclusion, the study demonstrates how this methodology effectively evaluates response non-uniformity along different sections of optical fibers.

Wiring networks diagnosis using K-Nearest neighbour classifier and dynamic time warping

ABSTRACT In this study, an effective diagnostic method for wiring networks based on reflectometry technique, the K-Nearest Neighbour (KNN) classifier, and Dynamic Time Warping (DTW) was developed. The proposed approach relies on a two-fold process: the offline process and the online process. In the offline process, basic circuit elements-based modelling and the Finite-Difference Time-Domain (FDTD) numerical method are employed to simulate Time Domain Reflectometry (TDR) and generate necessary datasets simultaneously. These datasets are then used to train and obtain classification and regression models. The DTW distance is combined with the KNN classifier to derive these models. In the online process, the models are utilised to identify, locate, and characterise faults in Wiring Networks Under Test based on their TDR response. Numerical and experimental results are presented to illustrate the performance and feasibility of the proposed method.

Enhanced artificial intelligence technique for soft fault localization and identification in complex aircraft microgrids

- In recent years, the aviation industry has witnessed a substantial integration of power electronics technology within Aircraft Microgrids (AMs). Consequently, the extension of electrical wiring networks has expanded, resulting in heightened intricacies within these systems. Therefore, the identification and location of faults in wiring networks have become an important topic in AMs to guarantee the safety of the electrical power systems. Time Domain Reflectometry (TDR) is widely used to locate and recognize electric wire faults. However, soft fault location on complex electrical networks using TDR is perplexing due to its weak effect on the reflected signal. Moreover, the existence of noise in the environment can worsen the TDR's performance. In this paper, a new approach based on TDR, along with the Subtractive Correlation Method (SCM) and Neural Network (NN), is proposed. The TDR response of the complex wiring network is determined using a newly proposed model based on the Finite Difference Time Domain (FDTD) method. The validity of the proposed model is established through experimentation including two distinct cable types also the introduced model notably enhances computational efficiency, a fact substantiated by our experimental findings and an extensive benchmarking against recent publications. These evaluations collectively underscore a significant reduction in computational time. Then, the reflected signal undergoes processing through SCM, a technique employed to amplify the subtle influence of the soft fault in two scenarios: one accompanied by noise and the other noise-free. Furthermore, NN is used to handle the inverse problem of localizing and characterizing the soft faults by their exact resistance values. Even within noisy environments, the proposed methodology excels in accurately locating and characterizing soft faults with a high degree of precision, all in real-time diagnostic scenarios.

Rod Insertion TDR for Detecting Corrosion Damage in Vertical Grounding Electrodes

Faults in a grounding network pose a serious threat to power system equipment protection and worker safety. A large number of vertical electrodes are used in a grounding network and break-points in these due to soil induced corrosion is one of the prime reasons for faults. We present a rod insertion time domain reflectometry (TDR) technique for vertical grounding electrode break-point detection. A secondary bare rod is inserted adjacent to the grounding electrode while a fast risetime pulse is continuously applied to excite a transverse electromagnetic (TEM) wave that propagates along the rods and reflects back from the break-point. The reflected signals for different depths of the secondary rod are analyzed to identify the location and severity of the break-point. We show that fault detection is possible without prior knowledge of soil electrical parameters. To show the feasibility of the proposed technique, a full wave simulation approach is used to evaluate the wideband input impedance of the grounding and inserted rod system and then FFT is applied to obtain TDR responses. The results show the method is capable of detecting faults for a wide range of soil conductivity for a system bandwidth of 300MHz.

Improved technical guide from physical model tests for TDR landslide monitoring

Time-domain reflectometry (TDR) can help observe the initiation and evolution of localized shear planes in both rock and soil slopes, and has temporal and spatial resolutions suitable for landslide monitoring. An improved and standardized technical guide for TDR cable installation and data interpretation is needed to utilize it better. TDR technique involves sending an electromagnetic pulse into a coaxial cable grouted in a pre-drilled borehole, and capturing the reflected signal from cable deformity, which is triggered by the localized shear deformation in the underground. This study developed a large direct shear box for testing TDR responses in various cable-grout-ground assemblies, in order to reliably model the cable-grout-ground interaction at the localized shear. Different combinations of cable type, grout condition, soil type, and shear bandwidth were tested using the new physical model to re-examine TDR's response to ground deformation in sliding mode. The results revealed some misconceptions in previous studies that used cable-grout assemblies without considering the entire interaction with the ground. The implications of the experimental results are carefully considered and new technical recommendations are provided for cable installation and data interpretation to foster the improved use of TDR. A new data reduction method that involves data filtering, differential waveform, and three-sigma rule is proposed to support the recommended installation setup for robust early detection of a localized shear plane.

Laboratory Approach to Determine Roller Compacted Concrete Mixture Electrical Characteristics Using a New Time-Domain Reflectometry Interpretation Method

This work deals with using time-domain reflectometry (TDR) to measure the electrical properties of roller compacted concrete (RCC). It is well known that TDR provides a non-destructive method to measure the dielectric constant toward an estimation of moisture content for soil materials. However, few studies have used TDR to determine the moisture content in concrete because of the inability to obtain TDR traces after the concrete hardens. To obtain TDR traces, a transmission circuit is initiated where a wave signal moves through the medium and reflects back in accordance with transmission line theory. In the literature, the TDR waveform has been interpreted empirically to estimate the relative permittivity (or dielectric constant) and electrical conductivity in a given material relative to the determination of associated water content. However, empirical models tend to ignore certain aspects related to the electrical properties of a medium, which has made interpretation of TDR measurements prone to systematic errors. In this paper, a new approach of test configuration and TDR response interpretation has been developed. For the test setup, the approach uses disposable metal probes that can be embedded into the concrete at different depths to obtain the TDR traces. The approach also employs the transmission line equation to estimate the dielectric constant, electrical conductivity, and reflectivity of an instrumented RCC mixture. These properties will affect the understanding of the RCC pavement behavior, especially curling and warping behavior, placement density, and development of long-term distresses.

Wiring networks diagnosis using time‐domain reflectometry and support vector machines

An efficient diagnosis method dedicated to embedded wiring network based on reflectometry technique is developed in this study. The proposed methodology is based on the two complementary steps. In the first step, the time-domain reflectometry (TDR) method is simulated, by RLCG (R: resistance, L: inductance, C: capacitance and G: conductance) circuit model and the numerical finite-difference time-domain method, and at the same time the datasets are created. In the second step, the support vector machine (SVM) algorithm is combined with a principal component analysis to identify the faults on wiring network from the TDR response. Two types of SVM models have been used in the diagnosis procedure: SVM classifiers and SVM regression models. In order to illustrate the performances and the feasibility of the proposed approach, numerical and experimental results are presented.

Wellhead based time domain reflectometry for casing integrity investigation

Wellbore integrity is one of the most critical factors for CO2 storage, subsurface resource extraction, and waste disposal. Wellbore integrity monitoring is challenging due to the general inaccessibility and high cost of well-logging operation in complex subsurface conditions. In this study, we tested a novel non-invasive approach for wellbore-integrity assessment based on time-domain reflectometry (TDR) method. With this method, a high-frequency electromagnetic pulse is sent into the borehole casing, and the reflected signals due to integrity-related impedance anomalies are recorded at the wellhead, providing rapid borehole integrity diagnosis without downhole deployment. Laboratory, numerical, and field experiments were conducted to prove the feasibility of this approach. The laboratory experiments with coaxial cable, resembling the typical wellbore, showed clear reflected signals from both of the damaged section and the end of the cable. Numerical sensitivity tests indicated that the TDR response is affected by the changes of the material in the borehole, including infilled fluids. Our field trial was conducted on an oil well (with a depth of 240 m) in the central valley region of southern California. The TDR records showed a clear reflection that matched both the calculated two-way travel time from the bottom of the borehole, as well as the numerical simulation. Our results suggested the feasibility of the TDR method for quick assessment of wellbore integrity based on wellhead only deployment.

A comprehensive framework of TDR landslide monitoring and early warning substantiated by field examples

Due to the climate change and hazardous geological environment in Taiwan, real-time landslide monitoring for safety of adjacent tribes or infrastructures is much conspicuous. Time domain reflectometry (TDR) is considered as valuable technology for continuously capturing dynamics and locations of shear deformations with high temporal and spatial coverages. In this study, a comprehensive framework was proposed by integrating up-to-date procedure of installation guide, data acquisition, sliding deformation identification, and early warning processing. The advantages of the proposed framework were then examined and substantiated via field cases. The first case is at a large colluvium slope in Lushan, central Taiwan. The colluvium is still active after heavy rainfall events, but limited evidence of sliding characteristic was collected previously by manual inclinometer logging. Therefore, the TDR framework was deployed and multiple landslide surfaces at great depths were timely observed. Movement of colluvium mass was thereby interpreted as a buckling fold. Additionally, creep occurred among the colluvium layer during the monsoon season because of strength weakening influenced by pore pressure fluctuations. Monitoring results of the second landslide case in Jufan, northern Taiwan, reported the dynamics of a thin sliding plane captured with TDR high temporal resolution. This unique behavior was further applied for devising of a novel early-warning strategy in terms of time-variation of TDR response, validating that the comprehensive framework of TDR landslide can effectively contribute to early warning.

An intelligent wire fault diagnosis approach using time domain reflectometry and pattern recognition network

ABSTRACTThe aim of this paper is to develop a new approach for wiring networks diagnosis. This approach is based on time domain reflectometry (TDR) and pattern recognition network all combined with a simple localisation and characterisation algorithm. The developed approach has the advantage of being fast, reliable and accurate. The TDR responses are generated using the analytical transmission line method. Furthermore, the manipulated data are reduced since only the main peaks are exploited. The performance of the proposed approach has been assessed using four cases, two using a simple Y-shaped network and two using a more complicated YY-shaped network. The obtained results are very satisfactory, and they show the applicability of the proposed approach for real systems.

Interpreting TDR Signal Propagation through Soils with Distinct Layers of Nonaqueous‐Phase Liquid and Water Content

Core Ideas The TDR response in the presence of NAPL transport was investigated in a homogeneous soil. The experimental framework was designed to simulate sharp NAPL fronts that moved in the soil. The TDR waveforms were analyzed to deduce the influence of NAPLs on TDR signal propagation. A general procedure to determine the location of an NAPL‐contaminated front was developed. Several studies have demonstrated that time‐domain reflectometry (TDR) has an enormous potential to detect and monitor nonaqueous‐phase liquids (NAPLs) in uniformly contaminated soils with reference to different‐textured soils and under different saturation conditions. Few attempts have been proposed to describe NAPL distribution by using TDR when a contaminated front propagates through a soil. In this study, the TDR response in the presence of NAPL‐contaminant transport processes was investigated in a homogeneous soil under confined conditions with a series of laboratory‐controlled tests. The laboratory procedure involved measurements of dielectric permittivity (εb) and electrical conductivity (ECb) and the acquisition of the reflected TDR waveforms. The experimental framework was designed to simulate sharp NAPL fronts that moved in the soil columns and generated zones of contrasting permittivity. During the experiments, different initial conditions were assumed, as well as different degrees of contamination, by varying the volumetric water (θw) and NAPL (θNAPL) contents. The acquired TDR waveforms were systematically analyzed to deduce the influence of NAPLs on TDR signal propagation. A general procedure to determine the location of an NAPL‐contaminated front within a soil column was developed. Equipment calibration, measurement accuracy, and error sources were investigated in relation to the experimental procedure setup and the column preparation conditions. The results show that there may be some difficulties when interpreting TDR signals to locate the NAPL front for two main reasons: first, the expected additional reflection at the interface is not always distinguishable; second, there may be problems even locating both the first peak and the reflection point at the end of the TDR probe, especially when the signal shifts from a low to a high impedance layer. The suggested methodology provides a tool to overcome such intrinsic difficulties in NAPL front detection during the propagation of the contaminant.

Performance improvements of wire fault diagnosis approach based on time‐domain reflectometry

The aim of this study is to improve the performance of a wire fault diagnosis approach based on time-domain reflectometry (TDR). In this approach, the TDR response collected from measurements for a given (faulty) coaxial cable network is used as a reference for the cable network model response computed by an accurate analytical transmission line (ATL) method. The ATL model is iteratively tuned according to the outcome of the optimisation algorithm proposed. The advantage of the proposed ATL method over the more classic finite difference time-domain method is the extremely faster computational time which represents a significant factor for the diagnosis of faults in wiring networks. The proposed ATL method accurately provides the features of the resistance-inductance-conductance-capacitance-based model of a RG-58 coaxial cable employed as a device under test. The ATL model has been validated by comparison with measurement based and circuit simulations. Furthermore, seven experiments have been investigated in order to evaluate the performance of the ATL method for the diagnosis of wiring networks. The obtained results reveal that the ATL method together with the efficient optimisation algorithm is a reliable, efficient and fast method for the diagnosis of wiring networks.

Localisation of faults in wiring networks using time domain reflectometry and adaptive neuro‐fuzzy inference system

The aim the present research is to develop a new approach for the localisation of faults in wiring networks based on adaptive neuro fuzzy inference system (ANFIS) and time domain reflectometry (TDR). In this approach a forward model has been developed and validated with measurements in order to generate a TDR response of any wiring network then the inverse problem is solved using ANFIS. The developed approach has been tested using a complex configuration that is YY-shaped network. The results show the efficiency and accuracy of the proposed approach.

Non-destructive diagnosis of wiring networks using time domain reflectometry and an improved black hole algorithm

The aim of this paper is to propose a new efficient and reliable approach for the diagnosis of wiring networks using Time Domain Reflectometry (TDR) and an Improved Black Hole (IBH) optimisation algorithm. A forward model based on RLCG parameters and Finite Difference Time Domain (FDTD) method is developed to simulate the TDR response for a given wiring network. The IBH algorithm is used to solve the inverse problem in order to detect, characterise and localise defects in wiring networks. Experimental studies are performed to show that the proposed approach can be implemented for the diagnosis of real-world systems. The results presented here demonstrate that the proposed approach using TDR and IBH is an efficient and reliable diagnosis approach for wiring networks.

An improved modeling of TDR signal propagation for measuring complex dielectric permittivity

Time domain reflectometry (TDR) is a measurement technique based upon transmission line theory. The solutions of transmission line equations are reformulated in terms of independent physical properties, instead of coupled per-unit-length circuit parameters. The complete TDR response is effectively modeled by a non-uniform transmission line using the non-recursive ABCD matrix approach. Approaches to calibrate line parameters and perform TDR measurements based upon such model are introduced with an example on dielectric spectroscopy. TDR modeling in terms of decoupled physical parameters and non-recursive algorithm allows more convenient calibration of line parameters and facilitates interpretation of TDR measurements.

Diagnosis of Multiple Wiring Faults Using Time-Domain Reflectometry and Teaching–Learning-Based Optimization

Time-domain reflectometry has proven to be one of the best methods for wiring network diagnosis, and it can easily be applied to the detection and localization of defects, while only requiring one access point to the wiring network. In this article, a novel approach combining the time-domain reflectometry response extracted from vector network analyzer measurements and the teaching–learning-based optimization technique is developed and applied to the diagnosis of wiring networks. The proposed approach consists of two steps. In the first step, propagation along the cables is modeled using the forward model. In the second step, teaching–learning-based optimization is used to solve the inverse problem to deduce physical information about the defects in the wiring network. The proposed approach has been successfully tested on several cases and for different configurations. Comparisons of the proposed time-domain reflectometry/teaching–learning-based optimization approach results with measurements reveal that this approach has a high potential and is very effective for wiring network diagnosis.

Effect of temperature, high pressure and freezing/thawing of dry-cured ham slices on dielectric time domain reflectometry response

Dielectric Time Domain Reflectometry (TDR) is a useful technique for the characterization and classification of dry-cured ham according to its composition. However, changes in the behavior of dielectric properties may occur depending on environmental factors and processing. The effect of temperature, high pressure (HP) and freezing/thawing of dry-cured ham slices on the obtained TDR curves and on the predictions of salt and water contents when using previously developed predictive models, was evaluated in three independent experiments. The results showed that at temperatures below 20°C there is an increase of the predicted salt content error, being more important in samples with higher water content. HP treatment caused a decrease of the reflected signal intensity due to the major mobility of available ions promoting an increase of the predicted salt content. Freezing/thawing treatment caused an increase of the reflected signal intensity due to the microstructural damages and the loss of water and ions, promoting a decrease of the predicted salt content.

Diagnosis of wiring networks using Particle Swarm Optimization and Genetic Algorithms

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for wiring network diagnosis allowing the detection, localization and characterization of faults. Two complementary steps are addressed. In the first step the direct problem is modeled using RLCG circuit parameters. Then the Finite Difference Time Domain method is used to solve the telegrapher’s equations. This model provides a simple and accurate method to simulate Time Domain Reflectometry responses. In the second step the optimization methods are combined with the wire propagation model to solve the inverse problem and to deduce physical information’s about defects from the reflectometry response. Several configurations are studied in order to demonstrate the applicability of each approach. Further, in order to validate the obtained results for both inversion techniques, they are compared with experimental measurements.

Localized functionally modified glass fibers with carbon nanotube networks for crack sensing in composites using time domain reflectometry

An electric time domain reflectometry (TDR) based sensing approach with an external parallel plate transmission line has been developed to evaluate high-frequency electromagnetic changes in composites due to applied load and internal damage. A model system of cross-ply glass fiber/vinyl ester composites with and without the selective integration of localized carbon nanotube (CNT) networks was studied where microcracking and delamination are introduced during tensile loading. A sizing technique has been used for localized functional modification using CNTs. The TDR sensing approach has been correlated with strain and acoustic emission (AE) measurements as well as micrographs of edge replicas capturing the damage state. Both the nanotube modified and baseline composites have similar mechanical properties and damage progression which is reflected in similar stress–strain plots, AE measurements and edge replica studies. However, the CNT introduced composites have enhanced strain and damage dependent TDR response. Hence, through localized functional modification of the composite electromagnetic properties using CNTs and the electromagnetic–mechanical property coupling of CNTs, it is possible to (1) increase TDR sensitivity to strain and (2) sense development of micro-scale cracks. This approach offers potential for use in existing composite structures or permanently integrated during the manufacturing process and is in situ and non-invasive.