Strain Signals Research Articles

Seismic prompt gravity strain signals in a layered spherical Earth

Seismic waves generated by an earthquake can produce dynamic perturbations in the Earth’s gravity field before the direct arrival of P-waves. Observations of these so-called prompt elasto-gravity signals by ground-based gravimeters and broadband seismometers have been reported for some large events, such as the 2011 MW9.1 Tohoku earthquake. Recent studies have introduced prompt gravity strain signals (PGSSs) as a new type of observable seismic gravity perturbation that can be used to measure the spatial gradient of the perturbed gravity field. Theoretically, these types of signals can be recorded by in-development instruments termed gravity strainmeters, although no successful detection has been reported as yet. Herein, we propose an efficient approach for PGSSs based on a multilayered spherical Earth model. We compared the simulated waveforms with analytical solutions obtained from a homogeneous half-space model, which has been used in earlier studies. This comparison indicates that the effect of the Earth’s structural stratification is significant. With the help of the new simulation approach, we also demonstrated how the PGSSs depend on the magnitude of the seismic source. We further conducted synthetic tests estimating earthquake magnitude using gravity strain signals to demonstrate the potential application of this type of signal in earthquake early warning systems. These results provide essential information for future studies on the synthesis and application of earthquake-induced gravity strain signals.

Redesign of Split-Hopkinson Tensile bar to eliminate spurious wave and verification of strain signal using FEM

To get the reliable dynamic material properties, the optimization of measuring apparatus such as SHTB (Split-Hopkinson Tensile Bar) is essential. SHTB apparatus is a machine that can acquire the material properties of a specimen for given high-speed situation in [Formula: see text] strain rate region. In this study, a new model improved the waveform is proposed by redesigning and optimizing the initial SHTB equipment. Complete reflected pulse was not obtained, and a spurious wave was measured in original model. To obtain reliable material properties, the spurious wave must be removed or avoided. Factors affecting spurious wave were determined as length of striker bar, length of incident bar, and diameter of striker bar & flange, and verified using LS-DYNA, one of the FEM (Finite-Element Method) software. The modified model was redesigned and manufactured based on FEM results. In FEM results, the incident and reflected pulse were completely improved. Also, overlapping with spurious wave was avoided in redesigned experiment results. Redesigned model was confirmed the residual wave after the incident pulse by a pneumatic launcher and various mechanical components. However, the residual wave didn’t affect the reflected pulse and the waveform was improved than the original model.

Dual-source gramian angular field method and its application on fault diagnosis of drilling pump fluid end

Under complex drilling working conditions, it is difficult to completely describe the working state of the drilling pump fluid end with a single-source signal to achieve a high fault diagnosis accuracy. This paper proposes to use dual-source signals (vibration signal and strain signal) to describe the working state of the fluid end. Especially, a dual-source gramian angular field (DS-GAF) method is proposed to fuse vibration-strain signals to provide more fault information. In DS-GAF method, the dual-source signal is converted to points in spherical coordinates, and the arccosine of the normalized amplitude of the two signals is assumed as the polar angle and the azimuthal angle, respectively. Three DS-GAF matrices are generated with the position of the points. These three matrices are respectively encoded as three colors (red, green, and blue) channels of the image to achieve signal fusion. A fault diagnosis method based on DS-GAF and ResNet for the drilling pump fluid end is proposed to improve the fault diagnosis accuracy under multiple working conditions. First, by the DS-GAF method, the vibration-strain signals of the fluid end are fused and converted into a fault diagnosis image dataset. Second, a fault diagnosis model based on ResNet is established and the effect of batch size on the performance of the fault diagnosis model is discussed. Finally, fault diagnosis experiments are carried out on the proposed fault diagnosis model and its performance is investigated. The results show that the diagnosis method proposed in this paper has the highest diagnostic accuracy of 98.97% for nine fault types under multiple operating conditions, with a maximum improvement of 18.91% compared to the original GAF method. The DS-GAF method for signal fusing proposed in this paper not only improves the accuracy of fluid end diagnosis, but also provides a reference for the signal-fusion diagnosis of other equipment.

Highly Stretchable, Biodegradable, and Recyclable Green Electronic Substrates.

As a steady stream of electronic devices being discarded, a vast amount of electronic substrate waste of petroleum-based nondegradable polymers is generated, raising endless concerns about resource depletion and environmental pollution. With coupled reagent (CR)-grafted artificial marble waste (AMW@CR) as functional fillers, polylactic acid (PLA)-based highly stretchable biodegradable green composite (AMW@CR-SBGC) is prepared, with elongation at break up to more than 250%. The degradation mechanism of AMW@CR-SBGC is deeply revealed. AMW@CR not only contributed to the photodegradation of AMW@CR-SBGC but also significantly promoted the water degradation of AMW@CR-SBGC. More importantly, AMW@CR-SBGC showed great potential as sustainable green electronic substrates and AMW@CR-SBGC-based electronic skin can simulate the perception of human skin to strain signals. The outstanding programmable degradability, recyclability, and reusability of AMW@CR-SBGC enabled its application in transient electronics. As the first demonstration of artificial marble waste in electronic substrates, AMW@CR-SBGC killed three birds with one stone in terms of waste resourcing, e-waste reduction, and saving nonrenewable petroleum resources, opening up vast new opportunities for green electronics applications in areas such as health monitoring, artificial intelligence, and security.

Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurements

Polycrystals illuminated by high-energy X-rays or neutrons produce diffraction patterns in which the measured diffraction peaks encode the individual single crystal strain states. While state of the art X-ray and neutron diffraction approaches can be used to routinely recover per grain mean strain tensors, less work has been produced on the recovery of higher order statistics of the strain distributions across the individual grains. In the setting of small deformations, we consider the problem of estimating the crystal elastic strain tensor probability distribution from diffraction data. For the special case of multivariate Gaussian strain tensor probability distributions, we show that while the mean of the distribution is well defined from measurements, the covariance of strain has a null-space. We show that there exist exactly 6 orthogonal perturbations to this covariance matrix under which the measured strain signal is invariant. In particular, we provide analytical parametrisations of these perturbations together with the set of possible maximum-likelihood estimates for a multivariate Gaussian fit to data. The parametric description of the null-space provides insights into the strain PDF modes that cannot be accurately estimated from the diffraction data. Understanding these modes prevents erroneous conclusions from being drawn based on the data. Beyond Gaussian strain tensor probability densities, we derive an iterative radial basis regression scheme in which the strain tensor probability density is estimated by a sparse finite basis expansion. This is made possible by showing that the operator mapping the strain tensor probability density onto the measured histograms of directional strain is linear, without approximation. The utility of the proposed algorithm is demonstrated by numerical simulations in the setting of single crystal monochromatic X-ray scattering. The proposed regression methods were found to robustly reject outliers and accurately predict the strain tensor probability distributions in the presence of Gaussian measurement noise.

Long‐term validation of virtual sensing of a railway bridge with ballasted superstructure

AbstractRailway bridges have a long lifespan, which is challenged by the constant development of vehicles leading to increased loads that they were not originally designed for. To ensure the longest possible use of existing structures, a sensor‐based structural health monitoring system can make a significant contribution. However, due to economic reasons and the inaccessibility of many points of interest, sensors cannot be installed everywhere. Therefore, in most cases, only a few sensors are available at a few points of interest, and methods that aim to reconstruct structural responses at unmeasured points from these measurements are referred to as virtual sensing. In this paper, we have analyzed 19,075 passages recorded on a steel trough bridge with a ballast superstructure and a span of 16.4 m, together with weather data. Our findings show that the influence of train type and speed has a significantly higher impact on the results than environmental factors. The investigation revealed that the model‐based analysis produced similar results to the data‐driven analysis concerning acceleration signals. However, when analyzing strain signals, the two approaches yielded distinctly different results.

Study on Design and Preparation of Conductive Polyvinylidene Fluoride Fibrous Membrane with High Conductivity via Electrostatic Spinning.

The novel conductive polyvinylidene fluoride (PVDF) fibrous membrane with high conductivity and sensitivity was successfully prepared via electrostatic spinning and efficient silver reduction technology. Based on the selective dissolution of porogen of polyvinylpyrrolidone (PVP), the porous PVDF fibrous membrane with excellent adsorbability and mechanical strength was obtained, providing a structure base for the preparation of conductive PVDF fibrous membrane with silver nanoparticles (AgNPs-PVDF). The Ag+ in the AgNO3 mixed solution with PVP was absorbed and maintained in the inner parts and surface of the porous structure. After the reducing action of ascorbic acid-mixed solution with PVP, silver nanoparticles were obtained tightly in an original porous PVDF fibrous membrane, realizing the maximum conductivity of 2500 S/m. With combined excellent conductivity and mechanical strength, the AgNPs-PVDF fibrous membrane effectively and sensitively detected strain signals of throat vocalization, elbow, wrist, finger, and knee (gauge factor of 23). The electrospun conductive AgNPs-PVDF combined the characteristics of low resistance, high mechanical strength, and soft breathability, which provided a new and effective preparation method of conductive fibers for practical application in wearable devices.

A high-precision strain seeding spacing monitoring system based on a combined bionic strain sensor and strain peak recognition algorithm

This study has discovered a novel monitoring quantity suitable for seeding spacing monitoring, which is the strain signal generated by the seed flow through the seed discharge port during the operation of the finger-clip seed metering device. The strain sensor output a dataset of approximately 30 electrical signals for each flow of the seed pick-up clip of the finger-clip seed metering device through the seed discharge port. The number of peak points in this data set is equivalent to the number of seeds sown, enabling accurate judgment of missed seeding or reseeding. This study addressed the problem that field vibrations tend to cause displacement of seed, which in turn triggers a change in strain direction. Through a combined bionic design, a scorpion body surface V-shaped crack structure (ultrasensitive sensing function) and a spider-web annular topology (omnidirectional equalization sensing function) were selected as bionic prototypes to develop a combined bionic strain sensor (CBSS). The test results showed that the CBSS combines the functional characteristics of both bionic structures and was more suitable for seed spacing monitoring. Compared to the conventional straight-line V-shaped crack strain sensor (CSS), CBSS improved the omnidirectional sensitivity by 21.54–35.57%. To mitigate the impact of ground frequency noise in the system causing fluctuations in the CBSS output signal, a strain peak recognition algorithm (SPRA) was developed using image processing techniques and Python. The SPRA effectively removed ground frequency noise using a gray-scale erosion algorithm and accurately recognized peak points in the electrical signal dataset through the Suzuki and CEV algorithms. Results showed that the SPRA reduced monitoring errors by 20.52% compared to the conventional direct data processing method (DDPM). Based on the CBSS and SPRA, a strain seeding spacing monitoring system (S-SMS) was developed. Bench tests revealed that the parameters of the combined bionic annular crack structure (crack depth h) of the CBSS significantly affected the monitoring error of the S-SMS, with optimal performance achieved at the h value of 82 μm. Field tests demonstrated that the CBSS accurately monitored the strain signal triggered by each seed with 98.51% accuracy, while the SPRA recognized missed seeding and reseeding with 98.64% and 98.42% accuracy, respectively. Compared to the conventional photoelectric diffuse reflection seeding monitoring system (P-SMS), the S-SMS reduced seeding monitoring errors in the field by 3.51%. This research can provide a new research path for the development of seeding spacing monitoring systems.

Flexible Silica/MXene/Natural rubber film strain sensors with island chain structure for Healthcare monitoring

The demand for flexible strain sensors with high sensitivity and durability has increased significantly. However, traditional sensors are limited in terms of their detection ranges and fabrications. In this work, a space stacking method was proposed to fabricate natural rubber (NR)/ Ti3C2Tx (MXene)/silica (SiO2) films that possessed exceptional electrical conductivity, sensitivity and reliability. The introduction of SiO2 into the NR/MXene composite enabled the construction of an “island-chain structure”, which promoted the formation of conductive pathways and significantly improved the conductivity of the composite. Specifically, the electrical conductivity of the NR/MXene/10 wt%SiO2 composite was enhanced by about 200 times compared to that of the NR/MXene composite alone (from 0.07 to 13.4 S/m). Additionally, the “island-chain structure” further enhanced the sensing properties of the NR/MXene/10 wt%SiO2 composite, as evidenced by its excellent sensitivity (GF = 189.2), rapid response time (102 ms), and good repeatability over 10,000 cycles. The fabricated device demonstrates an outstanding mechanical sensing performance and can accurately detect human physiological signals. Specifically, the device serves as a strain detector, recognizing different strain signals by monitoring the movement of fingers, arms, and thighs. This study provides critical insights into composite manufacturing with exceptional conductivity, flexibility and stability, which are essential properties for creating high-performance flexible sensors.

A deep feature learning method for remaining useful life prediction of drilling pumps

Remaining Useful Life (RUL) prediction of drilling pumps, pivotal components in fossil energy production, is essential for efficient maintenance and safe operation of such facilities. This paper introduces a deep feature learning method that combines a Convolutional Neural Network (CNN)-Convolutional Block Attention Module (CBAM) and a Transformer network into a parallel channel method to predict the RUL of drilling pumps. Specifically, two parallel channels independently extract time-frequency domain and time-domain features from strain signals and then proceed with degradation estimation through feature learning. The deep features derived independently from the two channels are subsequently amalgamated to predict the RUL of the drilling pump. The proposed method is validated by the operational data from four operating drilling pumps. The comparative analysis confirms the higher accuracy of the proposed method over several existing state-of-the-art approaches. Overall, the proposed method supports the safe and cost-saving-oriented operation and maintenance of drilling pumps.

Stretchable, self-healing, biocompatible, and durable ionogel for continuous wearable strain and physiological signal monitoring

Electronic textiles (e-textiles) have great potential for use in wearable systems worn next to skin for continuous physiological monitoring, owing to their flexibility and conformability to the body. However, challenges exist in the long-term functionality and durability of these materials. This work presents a highly stretchable, self-healing, and biocompatible chloride-based ionogel fabricated from polymerization of thioctic acid, and 1-n-Butyl-3-methylimidazolium chloride ([BMIM]Cl) ionic liquid. With its adaptable mechanical properties and tunable viscosity by simple dilution, the ionogel precursor can be either cast into an ionogel film used for strain sensing applications up to 500% strain, or directly sprayed as a coating for e-textile electrodes for biopotential signal monitoring. The ionogel can reform at room temperature, demonstrating 73.5% stress recovery, and 84.8% modulus recovery after a 30-minute healing period. When used as a spray barrier coating on e-textiles, the ionogel displayed high resistance to abrasion, and reformed after adding ethanol to the surface. The electrical properties of the ionogel coated e-textiles satisfies the performance range desired for textile sensing electrodes. Further development and application of this ionogel material system can address long-term durability concerns by extending the usable lifetime of the sensors for wearable applications.

Mechanical strain signal based early warning for failure of different prismatic lithium-ion batteries

Thermal runaway propagation (TRP) is the most challenging safety issue of lithium-ion battery systems. An early warning signal is promising for providing time to mitigate or prevent TRP and further disasters timely. This study investigates the strain characteristics of different format prismatic batteries during the TR/TRP processes, dividing the strain-changing trend into three stages according to the complex chemical composition interactions and TR features. Experimental results prove that the signal provides more than a 500s interval for a timely solution from the battery management system. Besides, the maximum strain increment (Δεmax)/capacity(Q)-Q equalization is quantitively analyzed. Moreover, Δεmax-Q and RI-Q equalizations are proposed to reveal the TR mechanical feature and guide the TR warning threshold definition of battery management systems (BMSs). Furthermore, the strain-changing trend and warning effect is also proved in TRP tests. TRP strain mechanism is unlocked from the mechanical deformation perspective. Battery deformation is opposite to the TRP direction, which guides accident analysis. This study proposes a cheap and reliable warning signal for an in-line configuration battery system, with only one strain gauge attached to the surface center of the first/last battery casing, which has more potential to guarantee the active safety of battery systems.

Dynamic Inversion Model of the Mooring Force on a Floating Bollard of a Sea Lock

Sea locks that connect inland canals and rivers to the open sea are crucial links that ensure the efficient navigation of ships. Floating bollards (FBs) are significant components of sea locks, and they are affected by factors such as large ships, speed of entry, and irregular mooring lines coupled with corrosion by chloride salts from seawater intrusion from the environment. These factors aggravate damage to metal structures, which seriously threatens the safety of FBs. Overloading of FBs by mooring forces caused by the illegal use of FBs for the braking of large ships that enter locks at excessive speed is the main cause of structural damage and overload failure for FBs. Controlling the dynamic mooring force acting on the FB is an important prerequisite to ensure the safe passage of a ship through a lock. It is impossible to perform real-time monitoring of the magnitude and direction of the mooring force on an FB by installing load-measuring equipment on the mooring line. Therefore, in this study, the structure of an FB in a sea lock project was taken as an example, and the mathematical relationships between the strain in the load-sensitive area of the FB and the mooring force and the mooring angle were quantified. A dynamic inversion model of the ship mooring force on an FB was proposed. This model used real-time feedback from the strain signal in the load-sensitive region of the FB structure to obtain information about the mooring force. The accuracy of the model was verified by conducting tests with a physical model of the topside structure of the FB and comparing the predicted results with the test data. The research results can lay a theoretical foundation for real-time monitoring of the structural response of an FB under the action of mooring forces and promote the development of intelligent methods for the operation and maintenance of a sea lock, which have important scientific significance and engineering value.

Strain gauge measurements of an oscillating heat pipe from startup to stable operation

Oscillating heat pipe (OHP) devices offer high heat flow (Q) and low thermal resistance (Rth) capabilities with convenient form factors for applications in spacecraft and terrestrial thermal management. Though prior measurements have used intrusive strain measurements in a single OHP turn to study the complex thermal-fluid mechanisms of OHP operation, minimally-intrusive external strain measurements on the casing of a multi-turn OHP have not been previously reported. Here, we affix strain gauges to the exterior casing of an ammonia filled flat plate OHP and use strain measurements to quantify the frequency response of ammonia vapor pressure fluctuations over a broad Q range. For all Q, the Fourier transformed strain signals referenced to the Q=0 W background are well-described by a low-pass filter fitting function with a low-frequency amplitude a and characteristic “knee” frequency c. In all cases, a increases with increasing Q due to the enhanced vapor pressure fluctuations at larger thermal loads. In the startup regime of Q<10W, we observe large temporal deviations in the strain response, large Rth>0.1K/W, and c values that increase with increasing Q. In the stable operating regime for Q near 100W, we observe a stable frequency response, a minimum value of Rth=0.02K/W, and Q-independent c values that saturate near 10Hz. Our strain gauge findings are consistent with a mechanism of intermittent boiling near startup followed by stable vapor plug/liquid slug motion at higher Q. Thus, our measurements show that externally placed and minimally invasive strain gauges canprovide diagnostic capabilities and additional insight into operation in both the startup and stable regimes for multi-turn, lightweight OHP devices used in aerospace and terrestrial thermal management applications.

Strain signal denoising based on adaptive Variation Mode Decomposition (VMD) algorithm

Addressing the problem of vulnerability of the directly measured signal in the field of strain weighing to the high-energy noise of similar frequency bands, an adaptive VMD algorithm is proposed from the perspective of signal separation for the decomposition and denoising of strain signal in the field of strain weighing. In this paper, the adaptive VMD algorithm is used to determine the optimal values of two key parameters, namely, the number of decomposition layers and the penalty factor, to avoid the blindness of parameter selection. The separation results are tested by parameters such as sample entropy, and then the original measurement signal is adaptively decomposed into multiple optimal intrinsic mode function components, and the effective components after extraction are reconstructed into new observation signals. The analysis results of the strain data collected at the weighing site show that the adaptive VMD algorithm can separate and extract the effective strain signal in line with the actual situation from the original strain signal mixed with noise and achieve the purpose of avoiding the interference of high-energy environmental noise with close frequency bands.

Experimental study on wayside monitoring method of train dynamic load based on strain of ballastless track slab

In the long-term service of ballastless track, which constantly bears the cyclic load from high-speed trains, it is important to obtain the long-term mechanical behavior of train dynamic load on the ballastless track. In this paper, a novel wayside monitoring method of train dynamic load is proposed, which takes CRTS III ballastless track as the research object and embeds Fiber Bragg grating (FBG) strain sensors into the track slab. The traditional wayside method installing strain gauges on the rail is used to compared with the sensors embedded in the track slab, which was tested in the field. Then, the time domain and frequency domain information of rail strain and slab strain are analyzed in detail, and the correlation analysis of signals is carried out. Finally, the calculation method of the running trains information and dynamic load is proposed. The results show that the vertical strain level of track slab is low under the action of high-speed train dynamic load. The time history curve shows that the adjacent wheel has obvious coupling effect on the slab strain signal, rather than rail strain. The frequency domain analysis of the signals indicates that the low-frequency components of the rail strain and slab strain have a strong correlation. However, the high-frequency components of train dynamic load are buffered out due to the effect of the fastener system. The mapping relationship between the track slab strain and the dynamic load can be found through calibration test, which provide a novel monitoring method of dynamic train load. The monitoring method can realize the real-time monitoring of train running information and record the long-term train dynamic load cycle law, and provide important parameters for the ballastless track boundary condition model.

Ultrahigh-resolution and ultra-simple fiber-optic sensor with resonant Sagnac interferometer.

The resonant fiber-optic sensor (RFOS) is well known for its high sensing resolution but usually suffers from high cost and system complexity. In this Letter, we propose an ultra-simple white-light-driven RFOS with a resonant Sagnac interferometer. By superimposing the output of multiple equivalent Sagnac interferometers, the strain signal is amplified during the resonance. A 3 × 3 coupler is employed for demodulation, by which the signal under test can be read out directly without any modulation. With 1 km delay fiber and ultra-simple configuration, a strain resolution of 28f ε/Hz at 5 kHz is demonstrated in the experiment, which is among the highest, to the best of our knowledge, resolution optical fiber strain sensors.

A New Method for Separating Temperature Effect of Bridge Strain Monitoring

Temperature has a significant influence on bridge strain monitoring data. To improve the accuracy of temperature effect separation in strain monitoring data, this paper proposes a temperature effect separation method comprising variational nonlinear chirp mode decomposition (VNCMD), principal component analysis (PCA) and blind source separation. Firstly, VNCMD was used to decompose the monitoring data of strain and temperature, and the intrinsic mode functions (IMF) of strain and temperature signals were obtained. Secondly, PCA was used to reduce the dimension of IMF, and the false components were eliminated to select the optimal components. After reducing the dimension, the components were used as the input of fast independent component analysis model for blind source separation. Finally, the feasibility and accuracy of the method was verified via the signal-to-noise ratio (SNR) in the simulated signal, and the separation results were evaluated using the Pearson correlation coefficient between the strain component and the corresponding temperature component in real bridge monitoring data. The proposed method performed better than the empirical mode decomposition (EMD) method. The signal-to-noise ratio (SNR) of VNCMD improved 51.80% for daily temperature difference effect and 32.41% for annual temperature difference effect in the numerical study, respectively; the correlation coefficients of VNCMD improved 52.90% for daily temperature difference effect and 4.26% for annual temperature difference effect in practical verification, respectively.

Temperature Decoupling of a Hydrogel‐Based Strain Sensor under a Dynamic Temperature Field

AbstractHydrogels possess several interesting characteristics and have attracted increasing attention for flexible interactive strain‐sensing. However, hydrogel strain sensors are easily influenced by temperature because of the intrinsic characteristics of the materials, thus, their sensing accuracy is significantly affected. Herein, a strategy is proposed to eliminate the influence of temperature by building an in‐situ hydrogel temperature sensor next to the strain sensor to monitor ambient temperature changes and simultaneously correct the strain signal. By introducing silicon nanoparticles and modified graphene, the hydrogel exhibits a good balance between conductivity and stretchability. The hydrogel strain sensor exhibits a working range of up to 1000% and a sensitivity of 8.1. It can monitor human movement and shows good stability. Moreover, the hydrogel‐based sensor demonstrates an impressive thermal response sensitivity (−7.16% °C−1). This bimodal sensor not only realizes the decoupling of the strain sensor from the temperature but protects the temperature sensor from the influence of strain. More importantly, the device is also able to accurately control the manipulator under dynamic temperatures, proving the feasibility of the design. This strategy provides a new method to eliminate the influence of temperature on strain sensing and assists in the development of the interactive‐sensing field.

Detection of Gas Pipeline Leakage Using Distributed Optical Fiber Sensors: Multi-Physics Analysis of Leakage-Fiber Coupling Mechanism in Soil Environment

Optical fiber sensors are newly established gas pipeline leakage monitoring technologies with advantages, including high detection sensitivity to weak leaks and suitability for harsh environments. This work presents a systematic numerical study on the multi-physics propagation and coupling process of the leakage-included stress wave to the fiber under test (FUT) through the soil layer. The results indicate that the transmitted pressure amplitude (hence the axial stress acted on FUT) and the frequency response of the transient strain signal strongly depends on the types of soil. Furthermore, it is found that soil with a higher viscous resistance is more favorable to the propagation of spherical stress waves, allowing FUT to be installed at a longer distance from the pipeline, given the sensor detection limit. By setting the detection limit of the distributed acoustic sensor to 1 nε, the feasible range between FUT and the pipeline for clay, loamy soil and silty sand is numerically determined. The gas-leakage-included temperature variation by the Joule-Thomson effect is also analyzed. Results provide a quantitative criterion on the installation condition of distributed fiber sensors buried in soil for the great-demanding gas pipeline leakage monitoring applications.