As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contr...Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.展开更多
The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibratio...The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.展开更多
The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed ...The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed site investigations and long-term monitoring of subsurface geological conditions.Understanding the temporal and spatial distributions of underground multi-field information is vital for successful engineering construction and effective utilization of urban underground space.In this study,a fiber optic nerve system(FONS)was utilized in the Tianfu New Area,Sichuan Province,China,to obtain comprehensive subsurface multi-physical information,including geological deformation,temperature,and surface hydrological data.The FONS incorporates three advanced fiber optic sensing techniques,i.e.fiber Bragg grating(FBG),Brillouin optical time domain reflectometry(BOTDR),and Raman optical time domain reflectometry(ROTDR).Fully-and quasi-distributed strain/temperature sensing cables have been installed in nine monitoring boreholes,covering various geological features such as plains,terraces,and areas within active fault zones.The field monitoring results confirm the feasibility of employing FONS for geological investigations within urban development zones,offering a valuable reference for future applications of this cost-effective technology in geohazard mitigation.展开更多
With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal c...With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.展开更多
The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)...The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.展开更多
Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology...Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.展开更多
We proposed a fiber optic high temperature sensor based on the Mach-Zehnder interference(MZI)structure,which is composed of two lengths of multi-mode fibers(MMFs),a length of few-mode fiber(FMF)and two sections of sin...We proposed a fiber optic high temperature sensor based on the Mach-Zehnder interference(MZI)structure,which is composed of two lengths of multi-mode fibers(MMFs),a length of few-mode fiber(FMF)and two sections of single-mode fibers(SMFs).Firstly,the two sections of MMFs were spliced with two sections of SMFs.Then,the MMFs were fused to two ends of FMF to form a symmetrically structured fiber-optic MZI structure.In this structure,the MMF served as the optical mode field coupling element,and the cladding and core of the FMF are the interference arm and the reference arm of the MZI structure,respectively.We investigated the sensor's response characteristics of the temperature and strain.The experimental results indicate that the sensor is sensitive to temperature variation,and the temperature response sensitivity is up to 61.4 pm/℃ in the range of 40-250℃,while the sensor has weak strain sensitivity,its strain sensitivity is only-0.72 pm/μe in the strain range of 0-1400μe.Moreover,the sensor has good stability and repeatability.In brief,the proposed fiber optic high temperature sensor has good properties,such as high sensitivity,compact structure,good stability and repeatability,which can be used for monitoring the temperature of submerged oil electric pump units under oil wells.展开更多
Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has seve...Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision FOGs.The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under vibration.In this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward.Particularly,the loop gain is extracted out by adding a gain-monitoring wave.By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation depth.The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 Hz.This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.展开更多
Vibration detection using sensors with both wide working frequency range,good sensitivity,and other good performances is a topic of great interest in fields such as inertial navigation,deep-sea fishing boat engines co...Vibration detection using sensors with both wide working frequency range,good sensitivity,and other good performances is a topic of great interest in fields such as inertial navigation,deep-sea fishing boat engines condition monitoring,seismic monitoring,attitude,and heading reference system,etc.This paper investigates two 6H-SIC MEMS diaphragms,one triangular and the other square,used in a fiber optic Fabry–Perot(FP)accelerometer in an experimental scenario.The triangular chip shows a wide working frequency range of 630 Hz–5300 Hz,a natural frequency of 44.3 k Hz,and a mechanical sensitivity of 0.154 nm/g.An optimal structure of the square chip used in a probe such as a fiber optic FP accelerometer also shows a wide working frequency range of 120 Hz–2300 Hz;a good sensitivity of 31.5 m V/g,a resonance frequency of7873 Hz,an accuracy of 0.96%F.S.,a frequency measurement error of 1.15%,and an excellent linearity of 0.9995.展开更多
Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study pr...Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study presents a state-of-the-art review of the DFOS applications for monitoring and assessing the deformation behavior of typical tunnel infrastructure,including bored tunnels,conventional tunnels,as well as immersed and cut-and-cover tunnels.DFOS systems based on Brillouin and Rayleigh scattering principles are both considered.When implementing DFOS monitoring,the fiber optic cable can be primarily installed along transverse and longitudinal directions to(1)measure distributed strains by continuously adhering the fiber to the structure’s surface or embedding it in the lining,or(2)measure point displacements by spot-anchoring it on the lining surface.There are four critical aspects of DFOS monitoring,including proper selection of the sensing fiber,selection of the measuring principle for the specific application,design of an effective sensor layout,and establishment of robust field sensor instrumentation.These four issues are comprehensively discussed,and practical suggestions are provided for the implementation of DFOS in tunnel infrastructure monitoring.展开更多
A method for in-situ stress measurement via fiber optics was proposed. The method utilizes the relationship between rock mass elastic parameters and in-situ stress. The approach offers the advantage of long-term stres...A method for in-situ stress measurement via fiber optics was proposed. The method utilizes the relationship between rock mass elastic parameters and in-situ stress. The approach offers the advantage of long-term stress measurements with high spatial resolution and frequency, significantly enhancing the ability to measure in-situ stress. The sensing casing, spirally wrapped with fiber optic, is cemented into the formation to establish a formation sensing nerve. Injecting fluid into the casing generates strain disturbance, establishing the relationship between rock mass properties and treatment pressure.Moreover, an optimization algorithm is established to invert the elastic parameters of formation via fiber optic strains. In the first part of this paper series, we established the theoretical basis for the inverse differential strain analysis method for in-situ stress measurement, which was subsequently verified using an analytical model. This paper is the fundamental basis for the inverse differential strain analysis method.展开更多
A data gathering system is designed for the interferometric fiber optic gyroscope (IFOG) of land strapdown inertial system. IFOG is tested and the testing curve is given. The test data of IFOG are analyzed with Allan ...A data gathering system is designed for the interferometric fiber optic gyroscope (IFOG) of land strapdown inertial system. IFOG is tested and the testing curve is given. The test data of IFOG are analyzed with Allan variance method and each error coefficient is identified. Furthermore, a random drift error model for IFOG is built by the method of time series analysis. The conclusion provides supports for improving IFOG design and compensating for errors of IFOG in practice.展开更多
Static ice pressure affects safe operation of hydraulic structures. However, current detection methods are hindered by the following limitations： poor real-time performance and errors owing to the partial pressure of...Static ice pressure affects safe operation of hydraulic structures. However, current detection methods are hindered by the following limitations： poor real-time performance and errors owing to the partial pressure of the surrounding wall on traditional electrical resistance strain bellow pressure sensors. We developed a fiber optic sensor with a special pressure bellow to monitor the static ice pressure on hydraulic structures and used the sensor to measure static pressure in laboratory ice growth and melting tests from -30℃ to 5℃. The sensor resolution is 0.02 kPa and its sensitivity is 2.74 × 10-4/kPa. The experiments suggest that the static ice pressure peaks twice during ice growth and melting. The first peak appears when the ice temperature drops to -15℃ owing to the liquid water to solid ice transition. The second peak appears at 0℃ owing to the thermal expansion of the ice during ice melting. The novel fiber optic sensor exhibits stable performance, high resolution, and high sensitivity and it can be used to monitor the static ice pressure during ice growth and melting.展开更多
To reduce the drift error existing in the output signal of fiber optic gyroscopes （FOG）, a mathematical model of the FOG output signal is set up; the error characteristics of the FOG output signal are analyzed, and ...To reduce the drift error existing in the output signal of fiber optic gyroscopes （FOG）, a mathematical model of the FOG output signal is set up; the error characteristics of the FOG output signal are analyzed, and semi-soft threshold filtering is chosen based on the comparison of hard threshold and soft threshold filtering. The semi-soft threshold wavelet package filtering method is applied in the filtering of the FOG output signal. Experiments of the stationary and dynamic FOG output signals filtered with the wavelet package analysis are carried out in a lab environment, respectively. Experiments done with the real-time measured FOG signal show that the method of semi-soft threshold wavelet package filtering reduces the mean square error from 5 （°）/h to 1 （°）/h, so it is effective in eliminating the white noises and the fractal noises existing in the FOG. The novel method proposed here is proved valid in reducing the FOG drift error, satisfying the technical demands of high precision and realtime processing.展开更多
The plasma resonance fiber optic sensor has a research values in theory and is widely used in engineering because of its simple structure and high sensitivity. It is a simple and sensitive method to measure the refrac...The plasma resonance fiber optic sensor has a research values in theory and is widely used in engineering because of its simple structure and high sensitivity. It is a simple and sensitive method to measure the refractive index with optical fiber plasma wave. We make use of this characteristic to manufacture the plasma resonance fiber optic sensor which can detect the cure of epoxy compo site. We study the method of testing the solutions which have different refractive index with plasma resonance fiber optic sensor. A fiber optic sensing probe which has reliable performance and convenient operation for detecting the refractive index has been designed. The system for detecting the solution refractive index is developed and used to measure the refractive index of epoxy during the different phases in the cure process. Result shows that this system is credible and stable, the parameters tested are in accord with the facts.展开更多
The resonator fiber optic gyro （R-FOG） ,which utilizes a resonance frequency change due to the Sagnac effect,is a promising candidate for the next generation inertial rotation sensor. In this study, an open-loop R-F...The resonator fiber optic gyro （R-FOG） ,which utilizes a resonance frequency change due to the Sagnac effect,is a promising candidate for the next generation inertial rotation sensor. In this study, an open-loop R-FOG is set up using phase modulation spectroscopy. First,the demodulation curve is obtained using a lock-in amplifier. From the demodulation signal,a gyro dynamic range of ± 4.2rad/s is obtained. Then,using different phase modulation frequencies,the open-loop gyro output signal is measured when the gyro is rotated clockwise or counterclockwise. The bias drift as a function of time is also measured. The fluctuation of the output over 5s is about 0.02rad/s. The drift can be reduced by taking countermeasures against system noise.展开更多
An optical length measuring system base on all-fiber optic interferometer is proposed. The theoretical analysis indicates that, when the two branches of the interferometer are equal, the output have the maximum cohere...An optical length measuring system base on all-fiber optic interferometer is proposed. The theoretical analysis indicates that, when the two branches of the interferometer are equal, the output have the maximum coherent intensity. Therefore, the optical length can be obtained by measuring the distance of collimator movement. Through the experiment and simulation, the impact of the signal-to-noise ratio and fluctuation of the coupling efficiency on null error has been obtained.展开更多
In the discipline of geotechnical engineering, fiber optic sensor based distributed monitoring has played an increasingly important role over the past few decades. Compared with conventional sensors, fiber optic senso...In the discipline of geotechnical engineering, fiber optic sensor based distributed monitoring has played an increasingly important role over the past few decades. Compared with conventional sensors, fiber optic sensors have a variety of exclusive advantages, such as smaller size, higher precision, and better corrosion resistance. These innovative monitoring technologies have been successfully applied for performance monitoring of geo-structures and early warning of potential geo- hazards around the world. In order to investigate their ability to monitor slope stability problems, a medium-sized model of soil nailed slope has been constructed in laboratory. The fully distributed Brillouin optical time-domain analysis （BOTDA） sensing technology was employed to measure the horizontal strain distributions inside the model slope. During model construction, a specially designed strain sensing fiber was buried in the soil mass. Afterward, the surcharge loading was applied on the slope crest in stages using hydraulic jacks and a reaction frame. During testing, an NBX-6o5o BOTDA sensing interrogator was used to collect the fiber optic sensing data. The test results have been analyzed in detail, which shows that the fiber optic sensors can capture the progressive deformation and failure pattern of the model slope. The limit equilibrium analyses were also conducted to obtain the factors ofsafety of the slope under different surface loadings. It is found that the characteristic maximum strains can reflect the stability of the model slope and an empirical relationship was obtained, This study verified the effectiveness of the distributed BOTDA sensing technology in performance monitoring of slope.展开更多
Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a ...Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.展开更多
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
基金support from the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City,University of Macao (Grant No.SKL-IoTSC (UM)-2021-2023/ORPF/A19/2022)the General Research Fund project from Research Grants Council of Hong Kong Special Administrative Region Government of China (Grant No.15214722)the Start-up Fund from The Hong Kong Polytechnic University (Grant No.BD88).
文摘Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.
基金Supported by the Scientific Research and Technology Development Project of Petrochina Southwest Oil and Gas Field Company(20230307-02)。
文摘The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.42225702 and 42077235).
文摘The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed site investigations and long-term monitoring of subsurface geological conditions.Understanding the temporal and spatial distributions of underground multi-field information is vital for successful engineering construction and effective utilization of urban underground space.In this study,a fiber optic nerve system(FONS)was utilized in the Tianfu New Area,Sichuan Province,China,to obtain comprehensive subsurface multi-physical information,including geological deformation,temperature,and surface hydrological data.The FONS incorporates three advanced fiber optic sensing techniques,i.e.fiber Bragg grating(FBG),Brillouin optical time domain reflectometry(BOTDR),and Raman optical time domain reflectometry(ROTDR).Fully-and quasi-distributed strain/temperature sensing cables have been installed in nine monitoring boreholes,covering various geological features such as plains,terraces,and areas within active fault zones.The field monitoring results confirm the feasibility of employing FONS for geological investigations within urban development zones,offering a valuable reference for future applications of this cost-effective technology in geohazard mitigation.
基金supports from China National Funds for Distinguished Young Scientists(62425505)National Natural Science Foundation of China(U22A20206)+1 种基金the China Postdoctoral Science Foundation(2023M731188)the Fundamental Research Funds for the Central Universities(2024BRA012).
文摘With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.
基金The Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42407250)the Fund from Research Centre for Resources Engineering towards Carbon Neutrality(RCRE)of The Hong Kong Polytechnic University(Grant No.No.1-BBEM)the Fund from Natural Science Foundation of Jiangsu Province(Grant No.BK20241211)。
文摘The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.
基金Supported by Key Laboratory of Space Active Optical-Electro Technology of Chinese Academy of Sciences(2021ZDKF4)。
文摘Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.
基金supported by the Scientific Research Program Funded by Shaanxi Provincial Education Department (No.15JK1573)the Postgraduate Innovation and Practice Ability Development Fund of Xi’an Shiyou University (No.YCS21211084)。
文摘We proposed a fiber optic high temperature sensor based on the Mach-Zehnder interference(MZI)structure,which is composed of two lengths of multi-mode fibers(MMFs),a length of few-mode fiber(FMF)and two sections of single-mode fibers(SMFs).Firstly,the two sections of MMFs were spliced with two sections of SMFs.Then,the MMFs were fused to two ends of FMF to form a symmetrically structured fiber-optic MZI structure.In this structure,the MMF served as the optical mode field coupling element,and the cladding and core of the FMF are the interference arm and the reference arm of the MZI structure,respectively.We investigated the sensor's response characteristics of the temperature and strain.The experimental results indicate that the sensor is sensitive to temperature variation,and the temperature response sensitivity is up to 61.4 pm/℃ in the range of 40-250℃,while the sensor has weak strain sensitivity,its strain sensitivity is only-0.72 pm/μe in the strain range of 0-1400μe.Moreover,the sensor has good stability and repeatability.In brief,the proposed fiber optic high temperature sensor has good properties,such as high sensitivity,compact structure,good stability and repeatability,which can be used for monitoring the temperature of submerged oil electric pump units under oil wells.
基金Fundamental Research Funds for the Central Universities(YWF-23-L-1225)National Natural Science Foundation of China(62201025)Chinese Aeronautical Establishment(2022Z037051001)。
文摘Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision FOGs.The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under vibration.In this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward.Particularly,the loop gain is extracted out by adding a gain-monitoring wave.By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation depth.The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 Hz.This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.
基金Project supported by the National Natural Science Foundation of China(Grant No.32473216)Ningbo Youth Science and Technology Innovation Leading Talent Project(Grant No.2023QL004)。
文摘Vibration detection using sensors with both wide working frequency range,good sensitivity,and other good performances is a topic of great interest in fields such as inertial navigation,deep-sea fishing boat engines condition monitoring,seismic monitoring,attitude,and heading reference system,etc.This paper investigates two 6H-SIC MEMS diaphragms,one triangular and the other square,used in a fiber optic Fabry–Perot(FP)accelerometer in an experimental scenario.The triangular chip shows a wide working frequency range of 630 Hz–5300 Hz,a natural frequency of 44.3 k Hz,and a mechanical sensitivity of 0.154 nm/g.An optimal structure of the square chip used in a probe such as a fiber optic FP accelerometer also shows a wide working frequency range of 120 Hz–2300 Hz;a good sensitivity of 31.5 m V/g,a resonance frequency of7873 Hz,an accuracy of 0.96%F.S.,a frequency measurement error of 1.15%,and an excellent linearity of 0.9995.
基金funding support from Rijkswaterstaat,the Netherlands,and European Union’s Horizon 2020 Research and Innovation Programme(Project SAFE-10-T under Grant No.723254)China Scholarship Council,and National Natural Science Foundation of China(Grant No.42225702).
文摘Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study presents a state-of-the-art review of the DFOS applications for monitoring and assessing the deformation behavior of typical tunnel infrastructure,including bored tunnels,conventional tunnels,as well as immersed and cut-and-cover tunnels.DFOS systems based on Brillouin and Rayleigh scattering principles are both considered.When implementing DFOS monitoring,the fiber optic cable can be primarily installed along transverse and longitudinal directions to(1)measure distributed strains by continuously adhering the fiber to the structure’s surface or embedding it in the lining,or(2)measure point displacements by spot-anchoring it on the lining surface.There are four critical aspects of DFOS monitoring,including proper selection of the sensing fiber,selection of the measuring principle for the specific application,design of an effective sensor layout,and establishment of robust field sensor instrumentation.These four issues are comprehensively discussed,and practical suggestions are provided for the implementation of DFOS in tunnel infrastructure monitoring.
基金the Project Support of NSFC(No.U19B6003-05 and No.52074314)。
文摘A method for in-situ stress measurement via fiber optics was proposed. The method utilizes the relationship between rock mass elastic parameters and in-situ stress. The approach offers the advantage of long-term stress measurements with high spatial resolution and frequency, significantly enhancing the ability to measure in-situ stress. The sensing casing, spirally wrapped with fiber optic, is cemented into the formation to establish a formation sensing nerve. Injecting fluid into the casing generates strain disturbance, establishing the relationship between rock mass properties and treatment pressure.Moreover, an optimization algorithm is established to invert the elastic parameters of formation via fiber optic strains. In the first part of this paper series, we established the theoretical basis for the inverse differential strain analysis method for in-situ stress measurement, which was subsequently verified using an analytical model. This paper is the fundamental basis for the inverse differential strain analysis method.
文摘A data gathering system is designed for the interferometric fiber optic gyroscope (IFOG) of land strapdown inertial system. IFOG is tested and the testing curve is given. The test data of IFOG are analyzed with Allan variance method and each error coefficient is identified. Furthermore, a random drift error model for IFOG is built by the method of time series analysis. The conclusion provides supports for improving IFOG design and compensating for errors of IFOG in practice.
基金supported by the National Natural Science Foundation of China(No.51279122)the Graduate Innovation Foundation of Taiyuan University of Technology(No.2013A019)
文摘Static ice pressure affects safe operation of hydraulic structures. However, current detection methods are hindered by the following limitations： poor real-time performance and errors owing to the partial pressure of the surrounding wall on traditional electrical resistance strain bellow pressure sensors. We developed a fiber optic sensor with a special pressure bellow to monitor the static ice pressure on hydraulic structures and used the sensor to measure static pressure in laboratory ice growth and melting tests from -30℃ to 5℃. The sensor resolution is 0.02 kPa and its sensitivity is 2.74 × 10-4/kPa. The experiments suggest that the static ice pressure peaks twice during ice growth and melting. The first peak appears when the ice temperature drops to -15℃ owing to the liquid water to solid ice transition. The second peak appears at 0℃ owing to the thermal expansion of the ice during ice melting. The novel fiber optic sensor exhibits stable performance, high resolution, and high sensitivity and it can be used to monitor the static ice pressure during ice growth and melting.
基金Pre-Research Program of General Armament Departmentduring the11th Five-Year Plan Period(No.51309020503)the National De-fense Basic Research Program of China(973 Program)(No.973-61334)+1 种基金the National Natural Science Foundation of China(No.50575042)Specialized Research Fund for the Doctoral Program of Higher Education ( No.20050286026).
文摘To reduce the drift error existing in the output signal of fiber optic gyroscopes （FOG）, a mathematical model of the FOG output signal is set up; the error characteristics of the FOG output signal are analyzed, and semi-soft threshold filtering is chosen based on the comparison of hard threshold and soft threshold filtering. The semi-soft threshold wavelet package filtering method is applied in the filtering of the FOG output signal. Experiments of the stationary and dynamic FOG output signals filtered with the wavelet package analysis are carried out in a lab environment, respectively. Experiments done with the real-time measured FOG signal show that the method of semi-soft threshold wavelet package filtering reduces the mean square error from 5 （°）/h to 1 （°）/h, so it is effective in eliminating the white noises and the fractal noises existing in the FOG. The novel method proposed here is proved valid in reducing the FOG drift error, satisfying the technical demands of high precision and realtime processing.
文摘The plasma resonance fiber optic sensor has a research values in theory and is widely used in engineering because of its simple structure and high sensitivity. It is a simple and sensitive method to measure the refractive index with optical fiber plasma wave. We make use of this characteristic to manufacture the plasma resonance fiber optic sensor which can detect the cure of epoxy compo site. We study the method of testing the solutions which have different refractive index with plasma resonance fiber optic sensor. A fiber optic sensing probe which has reliable performance and convenient operation for detecting the refractive index has been designed. The system for detecting the solution refractive index is developed and used to measure the refractive index of epoxy during the different phases in the cure process. Result shows that this system is credible and stable, the parameters tested are in accord with the facts.
文摘The resonator fiber optic gyro （R-FOG） ,which utilizes a resonance frequency change due to the Sagnac effect,is a promising candidate for the next generation inertial rotation sensor. In this study, an open-loop R-FOG is set up using phase modulation spectroscopy. First,the demodulation curve is obtained using a lock-in amplifier. From the demodulation signal,a gyro dynamic range of ± 4.2rad/s is obtained. Then,using different phase modulation frequencies,the open-loop gyro output signal is measured when the gyro is rotated clockwise or counterclockwise. The bias drift as a function of time is also measured. The fluctuation of the output over 5s is about 0.02rad/s. The drift can be reduced by taking countermeasures against system noise.
基金supported by the Fundamental Research Funds for the Central Universities under Grant No.E022050205
文摘An optical length measuring system base on all-fiber optic interferometer is proposed. The theoretical analysis indicates that, when the two branches of the interferometer are equal, the output have the maximum coherent intensity. Therefore, the optical length can be obtained by measuring the distance of collimator movement. Through the experiment and simulation, the impact of the signal-to-noise ratio and fluctuation of the coupling efficiency on null error has been obtained.
基金the financial support provided by the National Basic Research Program of China (973 Program) (Grant No. 2011CB710605)the National Natural Science Foundation of China (Grant Nos. 41102174, 41302217)supported by the National Key Technology R&D Program of China (Grant No. 2012BAK10B05)
文摘In the discipline of geotechnical engineering, fiber optic sensor based distributed monitoring has played an increasingly important role over the past few decades. Compared with conventional sensors, fiber optic sensors have a variety of exclusive advantages, such as smaller size, higher precision, and better corrosion resistance. These innovative monitoring technologies have been successfully applied for performance monitoring of geo-structures and early warning of potential geo- hazards around the world. In order to investigate their ability to monitor slope stability problems, a medium-sized model of soil nailed slope has been constructed in laboratory. The fully distributed Brillouin optical time-domain analysis （BOTDA） sensing technology was employed to measure the horizontal strain distributions inside the model slope. During model construction, a specially designed strain sensing fiber was buried in the soil mass. Afterward, the surcharge loading was applied on the slope crest in stages using hydraulic jacks and a reaction frame. During testing, an NBX-6o5o BOTDA sensing interrogator was used to collect the fiber optic sensing data. The test results have been analyzed in detail, which shows that the fiber optic sensors can capture the progressive deformation and failure pattern of the model slope. The limit equilibrium analyses were also conducted to obtain the factors ofsafety of the slope under different surface loadings. It is found that the characteristic maximum strains can reflect the stability of the model slope and an empirical relationship was obtained, This study verified the effectiveness of the distributed BOTDA sensing technology in performance monitoring of slope.
文摘Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.
