Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system ...The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.展开更多
Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and i...Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and international researchers have made significant advances in the areas of theory and methods,technology and systems,and ground experiments and flight tests.These advances have led to the development of OFS technology from the laboratory research stage to the engineering application stage.However,a few problems encountered in practical applications limit the wider application and further development of this technology,and thus urgently require solutions.This paper reviews the history of research on the deformation monitoring of flight vehicles.It examines various aspects of OFS-based deformation monitoring including the main varieties of OFS technology,technical advantages and disadvantages,suitability in aerospace applications,deformation reconstruction algorithms,and typical applications.This paper points out the key unresolved problems and the main evolution paradigms of engineering applications.It further discusses future development directions from the perspectives of an evolution paradigm,standardization,new materials,intelligentization,and collaboration.展开更多
With the development of high speed railway traffic, the structure health monitoring for high-speed rail is necessary due to the safety issue. Optical fiber sensing technology is one of the options to solve it. Stress ...With the development of high speed railway traffic, the structure health monitoring for high-speed rail is necessary due to the safety issue. Optical fiber sensing technology is one of the options to solve it. Stress vector information is the important index to make more reasonable judgments about railway safety. However, information sensed by lots of commercial optical sensors is scalar. According to the stress filed distribution of rail, this paper proposes a new type of stress vector sensor based on optical fiber sensing cable(OFSC) with a symmetrical seven optical fibers structure and analyzes the relations between angle resolution and distance between adjacent of optical fibers through finite-element software(ANSYS) simulation. Through reasonable distance configuration, the angle resolution of the OFSC can be improved, and thus stress vector information, including the stress magnitude and the angle of stress, can be more accurately obtained. The simulation results are helpful to configure OFSC for angle resolution improvement in actual practice, and increase the safety factor in high speed railway structure health monitoring.展开更多
With the proposal of a“smart battery,”real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical applications.However,many traditional sensing tech...With the proposal of a“smart battery,”real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical applications.However,many traditional sensing technologies suffer from low sensitivity,large size,and electromagnetic interference problems,rendering them unusable in the harsh and complicated electrochemical environments of batteries.The optical sensor is an alternative approach to realize multiple-parameter,multiple-point measurements simultaneously.Thus,it has garnered significant attention.Through analyzing these measured parameters,the state of interest can be decoded to monitor a battery's health.This review summarizes current progress in optical sensing techniques for batteries with respect to various sensing parameters,discussing the current limitations of optical fiber sensors as well as directions for their future development.展开更多
Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality c...Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality characteristics of SPOF limit their integration with traditional fiber optic sensors.This article introduces for the first time a flexible fiber optic vibration sensor based on laser interference technology,which can be applied to vibration measurement under high stretch conditions.This sensor utilizes elastic optical fibers made of polydimethylsiloxane(PDMS)as sensing elements,combined with phase generating carrier technology,to achieve vibration measurement at 50−260 Hz within the stretch range of 0−42%.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
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 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
To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measureme...To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.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
Fiber optic temperature sensors stand out in a variety of applications due to their small size,chemical resistance,and resistance to electromagnetic interference.The traditional optical fiber temperature sensor direct...Fiber optic temperature sensors stand out in a variety of applications due to their small size,chemical resistance,and resistance to electromagnetic interference.The traditional optical fiber temperature sensor directly places the sensing structure in the temperature to be measured,and uses the thermo-optical effect and thermal expansion effect of the SiO_(2)material that constitutes the sensing structure to achieve measurement,while the thermo-optical coefficient and thermal expansion coefficient of SiO_(2) are very small,which limits the high sensitivity response characteristics of the optical fiber temperature sensing structure.In order to solve the problem of low sensitivity of traditional optical fiber temperature sensors,a Mach-Zehnder interferometric temperature sensor with a liquid-encapsulated tapered microfiber is developed.The sensor converts the temperature change into a change in the refractive index of the liquid material and thus realizes the measurement of temperature.In the range of 25~50℃,as the temperature increases,the wavelength of the transmission spectrum shifts towards shorter wavelengths.Experimental results show that the sensitivity of the liquid encapsulated microfiber interferometric temperature sensor can reach-57.91 nk·nm^(-1).This sensor has great potential for applications in marine environmental monitoring,biomedical diagnosis,and aerospace.展开更多
Presented here is long-range distributed vibration sensing based on internal-modulation optical frequency domain reflectometry(OFDR).In the proposed system with internal modulation,a silicon-based photonic-chip laser i...Presented here is long-range distributed vibration sensing based on internal-modulation optical frequency domain reflectometry(OFDR).In the proposed system with internal modulation,a silicon-based photonic-chip laser is used as the laser source,and by controlling the output voltage curve of an arbitrary waveform generator to induce temperature change in the external cavity of the laser,a 10-GHz optical frequency tuning range is achieved.The complexity of the proposed internal-modulation system is lower than that of the traditional external-modulation OFDR system that combines a narrow-linewidth laser with a single-sideband modulator to achieve wavelength tuning.Cross-correlation analysis is used as a sensing mechanism to evaluate the similarity between Rayleigh scatter signals and to achieve vibration event localization.Experimental comparison is made of the vibration sensing performance of the external-and internal-modulation systems,and for a vibration event generated at a distance of 100.95 km,they locate it with a sensing spatial resolution of 43.0 m and 16.8 m,respectively.The results indicates that the proposed distributed vibration sensing based on internal modulation has better sensing performance and lower complexity compared to the traditional external-modulation system.In addition,the proposed system is single-ended and involves no optical amplification,which makes it very suitable for ultra-long-range sensing.展开更多
This paper investigates the deformation and fracture propagation of sandstone specimen under uniaxial compression using the distributed fiber optic strain sensing(DFOSS)technology.It shows that the DFOSS-based circumf...This paper investigates the deformation and fracture propagation of sandstone specimen under uniaxial compression using the distributed fiber optic strain sensing(DFOSS)technology.It shows that the DFOSS-based circumferential strains are in agreement with the data monitored with the traditional strain gage.The DFOSS successfully scans the full-field view of axial and circumferential strains on the specimen surface.The spatiotemporal strain measurement based on DFOSS manifests crack closure and elastoplastic deformation,detects initialization of microcrack nucleation,and identifies strain localization within the specimen.The DFOSS well observes the effects of rock heterogeneity on rock deformation.The advantage of DFOSS-based strain acquisition includes the high spatiotemporal resolution of signals and the ability of full-surface strain scanning.The introduction to the DFOSS technology yields a better understanding of the rock damage process under uniaxial compression.展开更多
Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most o...Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most of which are based on the global rock bolt response evaluated in pull-out tests.This paper presents a laboratory experimental setup aiming to capture the rock formation effect,while using distributed fiber optic sensing to quantify the effect of the confinement and the reinforcement pull-out behavior on a more local level.It is shown that the behavior along the sample itself varies,with certain points exhibiting stress drops with crack formation.Some edge effects related to the kinematic freedom of the grout to dilate are also observed.Regardless,it was found that the mid-level response is quite similar to the average response along the sample.The ability to characterize the variation of the response along the sample is one of the many advantages high-resolution fiber optic sensing allows in such investigations.The paper also offers a plasticity-based hardening load transfer function,representing a"slice"of the anchor.The paper describes in detail the development of the model and the calibration/determination of its parameters.The suggested model captures well the coupled behavior in which the pull-out process leads to an increase in the confining stress due to dilative behavior.展开更多
Anthropogenic activity-induced sinkholes pose a serious threat to building safety and human life nowadays.Real-time detection and early warning of sinkhole formation are a key and urgent problem in urban areas.This pa...Anthropogenic activity-induced sinkholes pose a serious threat to building safety and human life nowadays.Real-time detection and early warning of sinkhole formation are a key and urgent problem in urban areas.This paper presents an experimental study to evaluate the feasibility of fiber optic strain sensing nerves in sinkhole monitoring.Combining the artificial neural network(ANN)and particle image velocimetry(PIV)techniques,a series of model tests have been performed to explore the relationship between strain measurements and sinkhole development and to establish a conversion model from strain data to ground settlements.It is demonstrated that the failure mechanism of the soil above the sinkhole developed from a triangle failure plane to a vertical failure plane with increasing collapse volume.Meanwhile,the soil-embedded fiber optic strain sensing nerves allowed deformation monitoring of the ground soil in real time.Furthermore,the characteristics of the measured strain profiles indicate the locations of sinkholes and the associated shear bands.Based on the strain data,the ANN model predicts the ground settlement well.Additionally,micro-anchored fiber optic cables have been proven to increase the soil-to-fiber strain transfer efficiency for large deformation monitoring of ground collapse.展开更多
We have numerically and experimentally investigated the flow rate measurement of the pipeline based on the optical fiber.Employing the large eddy simulation(LES)model,we have quantitatively analyzed the pressure fluct...We have numerically and experimentally investigated the flow rate measurement of the pipeline based on the optical fiber.Employing the large eddy simulation(LES)model,we have quantitatively analyzed the pressure fluctuation of the pipe wall caused by the turbulent flow in the pipeline.The simulation results have shown that the standard deviation of pressure fluctuation was quadratic with the flow rate.We have verified the theoretical model by using a distributed optical fiber acoustic sensing(DAS)system in the flow rate range from 0.61 m/s to 2.42 m/s.The experimental results were consistent with the simulation results very well.Furthermore,to improve the measuring error at the low flow rate,we have employed the composite adaptive denoising algorithm to eliminate the background noise and system noise.The final results have shown that the minimum goodness of fit was improved from 0.962 to 0.997,and the variation of the quadratic coefficient significantly decreased by 93.25%.The measured flow rate difference was only 0.84%between different sensing points in repeated experiments.展开更多
A new kind of fiber optic oxygen sensing material based on the fluorescence quenching of Ru（bpy）3Cl2 was prepared by the themo-polymerization method. The ruthenium dye was immobilized in N, N-methylene bisacrylamide...A new kind of fiber optic oxygen sensing material based on the fluorescence quenching of Ru（bpy）3Cl2 was prepared by the themo-polymerization method. The ruthenium dye was immobilized in N, N-methylene bisacrylamide（MBBA） polymer by physically trapping while MBBA was covalently crosslinked on the glass micro-beads by NaHSO3-O2-MnSO4 initiator system. The lock-in amplifyication technology was used for the detection of their sensing properties. The influences of indicator concentration, glass micro-beads diameter, post polymerization time, concentration and reaction time of glutaraldehyde on the properties of sensing materials were studied. To optimize the influencing factors to the sensing materials, the indicator concentration of 0.7 g/L, glass micro-beads diameter of 0.3 mm, post polymerization time of 5 h were achieved. The immobilization stability of ruthenium dye and the performance of the sensing materials were improved by the new polymerization system. An absolute detection limit of 3×10-6 （V/V） and the response time of 10 s were obtained. This kind of sensing materials has good stability and their life time is 2 years.展开更多
This paperdetails experimental work done to quantify stress measurements made optically utilizing ordinary single mode optical fibers. Strain-induced changes of birefringence for ordinary single mode optical fiber res...This paperdetails experimental work done to quantify stress measurements made optically utilizing ordinary single mode optical fibers. Strain-induced changes of birefringence for ordinary single mode optical fiber responses are characterized against standard stress measurements in a well understand configuration. The experimental scheme for this work and the results are presented in detaial. In this paper, POssible applications for this transverse stress character of single mode fibers are also proposed.展开更多
Aiming at some security problems in railway running and the application condition of existing technology, this paper studies some issues of using fiber optic sensing technology in railway security monitoring. Through ...Aiming at some security problems in railway running and the application condition of existing technology, this paper studies some issues of using fiber optic sensing technology in railway security monitoring. Through field experiment measuring the strain of the rail and analyzing the experiment data, the method of diagnosing the health condition of rail and wheel is investigated.展开更多
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20375 and 62075151)the National Key Research and Development Program of China(Grant No.202103021223042).
文摘The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.
基金funded by the National Natural Science Foundation of China(51705024,51535002,51675053,61903041,61903042,and 61903041)the National Key Research and Development Program of China(2016YFF0101801)+4 种基金the National Hightech Research and Development Program of China(2015AA042308)the Innovative Equipment Pre-Research Key Fund Project(6140414030101)the Manned Space Pre-Research Project(20184112043)the Beijing Municipal Natural Science Foundation(F7202017 and 4204101)the Beijing Nova Program of Science and Technology(Z191100001119052)。
文摘Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and international researchers have made significant advances in the areas of theory and methods,technology and systems,and ground experiments and flight tests.These advances have led to the development of OFS technology from the laboratory research stage to the engineering application stage.However,a few problems encountered in practical applications limit the wider application and further development of this technology,and thus urgently require solutions.This paper reviews the history of research on the deformation monitoring of flight vehicles.It examines various aspects of OFS-based deformation monitoring including the main varieties of OFS technology,technical advantages and disadvantages,suitability in aerospace applications,deformation reconstruction algorithms,and typical applications.This paper points out the key unresolved problems and the main evolution paradigms of engineering applications.It further discusses future development directions from the perspectives of an evolution paradigm,standardization,new materials,intelligentization,and collaboration.
文摘With the development of high speed railway traffic, the structure health monitoring for high-speed rail is necessary due to the safety issue. Optical fiber sensing technology is one of the options to solve it. Stress vector information is the important index to make more reasonable judgments about railway safety. However, information sensed by lots of commercial optical sensors is scalar. According to the stress filed distribution of rail, this paper proposes a new type of stress vector sensor based on optical fiber sensing cable(OFSC) with a symmetrical seven optical fibers structure and analyzes the relations between angle resolution and distance between adjacent of optical fibers through finite-element software(ANSYS) simulation. Through reasonable distance configuration, the angle resolution of the OFSC can be improved, and thus stress vector information, including the stress magnitude and the angle of stress, can be more accurately obtained. The simulation results are helpful to configure OFSC for angle resolution improvement in actual practice, and increase the safety factor in high speed railway structure health monitoring.
基金the support from the National Key R&D Program of China(2021YFB2400300)the National Natural Science Foundation of China(Nos.51972131 and 5202780089).
文摘With the proposal of a“smart battery,”real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical applications.However,many traditional sensing technologies suffer from low sensitivity,large size,and electromagnetic interference problems,rendering them unusable in the harsh and complicated electrochemical environments of batteries.The optical sensor is an alternative approach to realize multiple-parameter,multiple-point measurements simultaneously.Thus,it has garnered significant attention.Through analyzing these measured parameters,the state of interest can be decoded to monitor a battery's health.This review summarizes current progress in optical sensing techniques for batteries with respect to various sensing parameters,discussing the current limitations of optical fiber sensors as well as directions for their future development.
文摘Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality characteristics of SPOF limit their integration with traditional fiber optic sensors.This article introduces for the first time a flexible fiber optic vibration sensor based on laser interference technology,which can be applied to vibration measurement under high stretch conditions.This sensor utilizes elastic optical fibers made of polydimethylsiloxane(PDMS)as sensing elements,combined with phase generating carrier technology,to achieve vibration measurement at 50−260 Hz within the stretch range of 0−42%.
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 the National Natural Science Foundation of China(Nos.61705027,62375031 and 52075131)the Chongqing Science and Technology Commission Basic Research Project(No.CSTC-2020jcyj-msxm0603)the Chongqing Municipal Education Commission Science and Technology Research Program(No.KJQN202000609)。
文摘To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.
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.
文摘Fiber optic temperature sensors stand out in a variety of applications due to their small size,chemical resistance,and resistance to electromagnetic interference.The traditional optical fiber temperature sensor directly places the sensing structure in the temperature to be measured,and uses the thermo-optical effect and thermal expansion effect of the SiO_(2)material that constitutes the sensing structure to achieve measurement,while the thermo-optical coefficient and thermal expansion coefficient of SiO_(2) are very small,which limits the high sensitivity response characteristics of the optical fiber temperature sensing structure.In order to solve the problem of low sensitivity of traditional optical fiber temperature sensors,a Mach-Zehnder interferometric temperature sensor with a liquid-encapsulated tapered microfiber is developed.The sensor converts the temperature change into a change in the refractive index of the liquid material and thus realizes the measurement of temperature.In the range of 25~50℃,as the temperature increases,the wavelength of the transmission spectrum shifts towards shorter wavelengths.Experimental results show that the sensitivity of the liquid encapsulated microfiber interferometric temperature sensor can reach-57.91 nk·nm^(-1).This sensor has great potential for applications in marine environmental monitoring,biomedical diagnosis,and aerospace.
基金supported by the 34th Research Institute of CETC Funding(Grant No.K134002021S604)the New Technology Research University Cooperation Project of the 34th Research Institute of CETC(Grant No.2021-1200-05-001900).
文摘Presented here is long-range distributed vibration sensing based on internal-modulation optical frequency domain reflectometry(OFDR).In the proposed system with internal modulation,a silicon-based photonic-chip laser is used as the laser source,and by controlling the output voltage curve of an arbitrary waveform generator to induce temperature change in the external cavity of the laser,a 10-GHz optical frequency tuning range is achieved.The complexity of the proposed internal-modulation system is lower than that of the traditional external-modulation OFDR system that combines a narrow-linewidth laser with a single-sideband modulator to achieve wavelength tuning.Cross-correlation analysis is used as a sensing mechanism to evaluate the similarity between Rayleigh scatter signals and to achieve vibration event localization.Experimental comparison is made of the vibration sensing performance of the external-and internal-modulation systems,and for a vibration event generated at a distance of 100.95 km,they locate it with a sensing spatial resolution of 43.0 m and 16.8 m,respectively.The results indicates that the proposed distributed vibration sensing based on internal modulation has better sensing performance and lower complexity compared to the traditional external-modulation system.In addition,the proposed system is single-ended and involves no optical amplification,which makes it very suitable for ultra-long-range sensing.
基金support from the Institute of Crustal Dynamics,China Earthquake Administration(Grant No.ZDJ2016-20 and ZDJ2019-15)。
文摘This paper investigates the deformation and fracture propagation of sandstone specimen under uniaxial compression using the distributed fiber optic strain sensing(DFOSS)technology.It shows that the DFOSS-based circumferential strains are in agreement with the data monitored with the traditional strain gage.The DFOSS successfully scans the full-field view of axial and circumferential strains on the specimen surface.The spatiotemporal strain measurement based on DFOSS manifests crack closure and elastoplastic deformation,detects initialization of microcrack nucleation,and identifies strain localization within the specimen.The DFOSS well observes the effects of rock heterogeneity on rock deformation.The advantage of DFOSS-based strain acquisition includes the high spatiotemporal resolution of signals and the ability of full-surface strain scanning.The introduction to the DFOSS technology yields a better understanding of the rock damage process under uniaxial compression.
基金funding support from the Israeli Ministry of Housing and Construction(Grant No.2028286).
文摘Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most of which are based on the global rock bolt response evaluated in pull-out tests.This paper presents a laboratory experimental setup aiming to capture the rock formation effect,while using distributed fiber optic sensing to quantify the effect of the confinement and the reinforcement pull-out behavior on a more local level.It is shown that the behavior along the sample itself varies,with certain points exhibiting stress drops with crack formation.Some edge effects related to the kinematic freedom of the grout to dilate are also observed.Regardless,it was found that the mid-level response is quite similar to the average response along the sample.The ability to characterize the variation of the response along the sample is one of the many advantages high-resolution fiber optic sensing allows in such investigations.The paper also offers a plasticity-based hardening load transfer function,representing a"slice"of the anchor.The paper describes in detail the development of the model and the calibration/determination of its parameters.The suggested model captures well the coupled behavior in which the pull-out process leads to an increase in the confining stress due to dilative behavior.
基金support provided by the National Natural Science Foundation of China(Grant Nos.42225702,and 42077232)the Open Research Project Program of the State Key Laboratory of Internet of Things for Smart City(University of Macao)(Grant No.SKL-IoTSC(UM)-2021-2023/ORP/GA10/2022).
文摘Anthropogenic activity-induced sinkholes pose a serious threat to building safety and human life nowadays.Real-time detection and early warning of sinkhole formation are a key and urgent problem in urban areas.This paper presents an experimental study to evaluate the feasibility of fiber optic strain sensing nerves in sinkhole monitoring.Combining the artificial neural network(ANN)and particle image velocimetry(PIV)techniques,a series of model tests have been performed to explore the relationship between strain measurements and sinkhole development and to establish a conversion model from strain data to ground settlements.It is demonstrated that the failure mechanism of the soil above the sinkhole developed from a triangle failure plane to a vertical failure plane with increasing collapse volume.Meanwhile,the soil-embedded fiber optic strain sensing nerves allowed deformation monitoring of the ground soil in real time.Furthermore,the characteristics of the measured strain profiles indicate the locations of sinkholes and the associated shear bands.Based on the strain data,the ANN model predicts the ground settlement well.Additionally,micro-anchored fiber optic cables have been proven to increase the soil-to-fiber strain transfer efficiency for large deformation monitoring of ground collapse.
基金supported in part by the National Natural Science Foundation of China(Grant No.U22A20206)the Key Research and Development Plan Project of Hubei Province,China(Grant No.2022BAA004)Zhejiang Provincial Market Supervision Bureau Young Eagle Plan Project,China(Grant No.CY2022228).
文摘We have numerically and experimentally investigated the flow rate measurement of the pipeline based on the optical fiber.Employing the large eddy simulation(LES)model,we have quantitatively analyzed the pressure fluctuation of the pipe wall caused by the turbulent flow in the pipeline.The simulation results have shown that the standard deviation of pressure fluctuation was quadratic with the flow rate.We have verified the theoretical model by using a distributed optical fiber acoustic sensing(DAS)system in the flow rate range from 0.61 m/s to 2.42 m/s.The experimental results were consistent with the simulation results very well.Furthermore,to improve the measuring error at the low flow rate,we have employed the composite adaptive denoising algorithm to eliminate the background noise and system noise.The final results have shown that the minimum goodness of fit was improved from 0.962 to 0.997,and the variation of the quadratic coefficient significantly decreased by 93.25%.The measured flow rate difference was only 0.84%between different sensing points in repeated experiments.
基金the Project of National Nature Science Foundation of China(No.60377032)Key Project of National Nature Science Foundation of China(No.60537050)
文摘A new kind of fiber optic oxygen sensing material based on the fluorescence quenching of Ru（bpy）3Cl2 was prepared by the themo-polymerization method. The ruthenium dye was immobilized in N, N-methylene bisacrylamide（MBBA） polymer by physically trapping while MBBA was covalently crosslinked on the glass micro-beads by NaHSO3-O2-MnSO4 initiator system. The lock-in amplifyication technology was used for the detection of their sensing properties. The influences of indicator concentration, glass micro-beads diameter, post polymerization time, concentration and reaction time of glutaraldehyde on the properties of sensing materials were studied. To optimize the influencing factors to the sensing materials, the indicator concentration of 0.7 g/L, glass micro-beads diameter of 0.3 mm, post polymerization time of 5 h were achieved. The immobilization stability of ruthenium dye and the performance of the sensing materials were improved by the new polymerization system. An absolute detection limit of 3×10-6 （V/V） and the response time of 10 s were obtained. This kind of sensing materials has good stability and their life time is 2 years.
文摘This paperdetails experimental work done to quantify stress measurements made optically utilizing ordinary single mode optical fibers. Strain-induced changes of birefringence for ordinary single mode optical fiber responses are characterized against standard stress measurements in a well understand configuration. The experimental scheme for this work and the results are presented in detaial. In this paper, POssible applications for this transverse stress character of single mode fibers are also proposed.
文摘Aiming at some security problems in railway running and the application condition of existing technology, this paper studies some issues of using fiber optic sensing technology in railway security monitoring. Through field experiment measuring the strain of the rail and analyzing the experiment data, the method of diagnosing the health condition of rail and wheel is investigated.
