Ultra-weak Fiber Bragg Gratings Research Articles

Quasi-distributed acoustic sensing based on optical path difference demodulation for high-frequency and large-amplitude using an optical fiber interferometer array.

The phase-sensitive optical time domain reflectometry scheme has attracted great research interest in distributed acoustic sensing (DAS) but suffers from a trade-off between the dynamic range and signal frequency. In this paper, an optical path difference (OPD) demodulation method is applied to an interferometer array composed of an ultra-weak fiber Bragg grating (UWFBG) array and an imbalanced Michelson interferometer to solve this problem. As far as we know, this is the first time that OPD demodulation has been applied to DAS. The UWFBG array is interrogated by a frequency-modulated pulse, and the acoustic signal sensed by the fiber between any two adjacent UWFBGs can be retrieved by demodulating the variation of residual OPD of the interferometer formed by them. The proposed method is analyzed theoretically and validated experimentally; compared with the phase demodulation method, a 100-times boost in bandwidth is achieved for a signal with an amplitude of 0.4 µε. Results also show that the proposed method offers increasing signal-to-noise ratios as the frequency increases.

Research on gas pipeline leakage identification based on SE-2DCNN with ultra-weak fiber Bragg grating distributed acoustic sensing system

Abstract A new method for identifying gas pipeline leaks using an ultra-weak fiber Bragg grating (UWFBG) distributed acoustic sensing (DAS) system is proposed. To enhance the accuracy of gas leakage detection, an improved convolutional neural network (CNN) incorporating a two-dimensional structure based on the squeeze-and-excitation (SE) attention mechanism was introduced. The principle of acoustic leakage sensing using this technology is explained in detail, and an experimental setup simulating gas pipeline leaks is constructed. During this process, 9,340 DAS data points across varying leakage volumes and pipeline pressures were collected to create ten distinct datasets. The Mel-frequency cepstral coefficients (MFCC) are employed as the feature input for the optimized SE-2DCNN model, which performs identification tasks. The results show that this optimized model achieves an average leakage identification accuracy of 95.33%, demonstrating superior performance over other methods. This approach offers a robust reference for accurately detecting gas pipeline leakages.

Enhancing the performance of DAS using a dual-wavelength UWFBG array with alternating arrangement

We propose a novel distributed acoustic sensor (DAS) based on a dual-wavelength ultra-weak fiber Bragg grating (UWFBG) array. The UWFBGs in the array have two wavelengths that are arranged alternately. Two double-pulses with different wavelengths and staggered launching times are used for sensing in the UWFBG array. This approach successfully overcomes the trade-off between spatial resolution and sensing distance in the traditional UWFBG-based DAS and effectively doubles the maximum achievable sampling rate constrained by the sensing distance. The crosstalk of different wavelengths is avoided by time division multiplexing. Comprehensive analyses and simulations are conducted to validate the performance enhancements, compared to the traditional method. In our proof-of-concept experiments, employing a 2 km alternating arranged dual-wavelength UWFBG array, we achieve a spatial resolution enhancement from 10 m to 5 m, expand the dynamic strain measurement range from 34.2 rad to 68.2 rad, and increase the frequency response from 9 kHz to 19 kHz.

Application of ultra-weak fiber Bragg grating sensing array to road vehicle detection

Road vehicle detection is a significant task for tackling traffic issues in intelligent transportation systems (ITS). In this paper, we introduce ultra-weak fiber Bragg grating (UWFBG) sensing technology for road vehicle flow, vehicle axle and wheelbase detection. We carry out pilot installation of UWFBG sensing optic cables in the asphalt base of the road in a new-built expressway. As opposed to the conventional linear installation along the road running direction, the deployment of cables is mainly perpendicular to the road. Using the acquired vehicle data, we analyze signal characteristics and propose real-time data processing algorithms based on identification and classification of peaks/valleys of vehicle waveforms as well as cross-correlation calculation. We test the proposed method in different scenes. Test results show that the calculated wheelbases achieve good accuracy (relative errors are <1 %) and the algorithms can well distinguish individual vehicle in complex vehicle conditions.

Novel mining conveyor monitoring system based on quasi-distributed optical fiber accelerometer array and self-supervised learning

Belt conveyors in mining are crucial, with downtime leading to significant losses and safety hazards. Unplanned shutdowns often result from idler failures. To address this, an online monitoring system for continuous idler health assessment is proposed. Considering the large number and dense spatial distribution of idlers over long distances, this work presents a system that utilizes a quasi-distributed optical fiber accelerometer array. This array incorporates phase-sensitive optical time domain reflectometry (Phase-OTDR) interrogation technology and ultra-weak fiber Bragg gratings (UWFBGs) to effectively capture idler vibrations. The designed array achieves high-sensitivity vibration sensing with a sensitivity of 2.4 rad/g and a resolution of 1.7 mg/Hz. After collecting the vibrations of idlers by the designed accelerometer array, an automatic fault classification algorithm based on self-supervised learning (SSL) is introduced, which requires only a small number of labeled samples. By leveraging large amount of unlabeled data in the pretext task, the algorithm efficiently extracts latent features from the quasi-distributed accelerometer array. A diagnosis accuracy of 95.37 % can be achieved on a seven-class classification task with only 3.6 % labeled data (16 samples/class). This system offers a promising solution for idler monitoring, combining high sensitivity, distributed measurement capabilities, enhanced security, and superior fault detection accuracy.

Direction-Finding Study of a 1.7 mm Diameter Towed Hydrophone Array Based on UWFBG.

This paper investigates a 1.7 mm diameter ultra-weak fiber Bragg grating (UWFBG) hydrophone towed array cable for acoustic direction finding. The mechanism of the underwater acoustic waves received by this integrated-coating sensitizing optical cable is deduced, and it is shown that the amplitude of its response varies with the direction of the sound wave. An anechoic pool experiment is carried out to test the performance of such a hydrophone array. The test array is a selection of six sensing fibers, each of which is coiled into 9 cm diameter fiber ring suspended in the water to receive acoustic signals. An average sensitivity of -141.2 dB re rad/μPa at frequencies from 2.5 kHz to 6.3 kHz was achieved, validating the detection of the azimuth of underwater acoustic waves. The ultra-thin towing cable system, with free structure, high sensitivity, and underwater target-detection capability has demonstrated great potential for future unmanned underwater vehicle (UUV) applications.

Multi-parameter information detection of aircraft taxiing on an airport runway based on an ultra-weak FBG sensing array.

Tracking the moving state and analyzing the distribution feature of an aircraft taxiing on the pavement are crucial for runway safety management. One challenge is to provide a reliable methodology that would enable such needs to be addressed in long-distance range runway with high sensitivity and spatial resolution. This paper proposed a novel monitoring approach for acquiring multi-parameter information of aircraft taxiing on airport runway based on distributed vibration detected by ultra-weak fiber Bragg grating (FBG) sensing array. For an actual engineering equipped with the smart runway system, the ultra-weak FBG sensing technology was applied to collect the distributed vibration responses of taxiing aircraft in takeoff and landing stages. The monitoring data of the vibration sensing optical cables beneath the entire pavement were directly utilized to evaluate the identification feasibility and effectiveness of taxiing speed and position, landing distribution, and taxiing acceleration and distance based on samples of five representative aircraft types. The results indicated that the speed, position, aiming point offset, acceleration changes and probability distribution of the taxiing distance can be revealed based on actual measurements concerning the distribution of vibration intensity with respect to space and time. Moreover, the analysis demonstrated that the experimental results based on ultra-weak FBG sensing array was able to account for the characteristic of different aircraft types participating in the study and the current operating conditions of the airport.

Adaptive Vibration Monitoring of Railway Track Structures Using the UWFBG by the Identification of Train-Load Patterns

Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected monitoring data can be huge with the great number of sensors. Even though the train-induced vibration responses of urban railway tracks constitute the most informative and crucial component, they comprised less than 7% of the total operational period. This is mainly attributed to the temporal sparsity of commuting trains. Consequently, the majority of the stored data consisted of low-informative environmental noise and interference excitation data, leading to an inefficient structural health monitoring (SHM) system. To address this issue, this paper introduced an adaptive monitoring strategy for railway track structures, which is capable of identifying train-load patterns by leveraging deep learning techniques. Inspired by image semantic segmentation, a U-net model with one-dimensional convolution layers (U-net-1D) was developed for the pointwise classification of vibration monitoring data. The proposed model was trained and validated using a dataset obtained from an actual urban railway track in China. Results indicated that the proposed method outperforms the traditional dual-threshold method, achieving an Intersection over Union (IoU) of 94.27% on the segmentation task of the test dataset.

Direct detection Φ-OTDR based on UWFBG array using linear-phase-modulated double-pulse

We propose what we believe to be a novel direct detection phase-sensitive optical time-domain reflectometry (Φ-OTDR) based on ultra-weak fiber Bragg grating (UWFBG) array to achieve distributed vibration measurements with exceptional sensitivity and remarkable stability. Our system employs a pulse modulator to generate a double pulse and achieves linear phase modulation of one pulse by one cycle through a phase modulator. The phase change can be quantitatively demodulated using our proposed N-step phase-shifted demodulation algorithm. This method effectively mitigates the influence of phase noise of the laser and the pulse modulator, while also eliminating fluctuations in the half-voltage of the phase modulator. Compared with the existing phase modulation methods, our method avoids stringent requirements for the stability and precision of phase modulation. Moreover, we propose a phase-shifted approximation method, breaking the limitation of sensing length on the traditional differential approximation method and improving the accuracy significantly. The technique's effectiveness is experimentally demonstrated on a 1 km UWFBG array with a reflectivity of −40 dB to −45 dB and a spatial resolution of 10 m. Vibrations with different amplitudes are measured quantitatively with good linearity. The low-frequency self-noise is greatly suppressed and the overall self-noise is −54.3 dB rad2/Hz.

High-sensitivity water leakage detection and localization in tunnels using novel ultra-weak fiber Bragg grating sensing technology

Water leakage poses a serious threat to tunnel integrity and safety, making early detection critical. However, traditional techniques struggle with high-sensitivity leak monitoring. This paper presents a novel super absorbent polymer (SAP)-coated ultra-weak fiber Bragg grating (UWFBG) strain sensing cable for enhanced water leakage detection and localization in tunnels. The cable’s response and resulting morphological/strain changes upon leakage contact are characterized. When water interacts with the SAP-coated region, the polymer rapidly absorbs it and expands, producing a significant localized strain reduction. The cable demonstrates the repeatability for leakage sensing, addressing existing challenges. Tunnel model tests establish it can discriminate single-point, multi-point and linear leakages. Analysis of strain variations under different leakage rates, down to 1.5 mL/min, confirms strong performance for early warning. Installation along tunnel joints also enables leakage detection. In summary, the distributed SAP-UWFBG cable sensing system offers high sensitivity, real-time leakage monitoring capabilities critical for ensuring tunnel structural integrity and safety through its fast response to even minor leakage events.

Comparative Investigation of Axial Bearing Performance and Mechanism of Continuous Flight Auger Pile in Weathered Granitic Soils

Axial bearing performance and mechanism of continuous flight auger (CFA) pile in weathered granitic soils, i.e., a widespread special soil in South China, were investigated by field test in this study. Load–settlement responses of four CFA piles were examined, and evolutions of shaft/base resistances were captured by ultra-weak fiber Bragg gratings (UWFBG) with a reflectivity ≤−40 dB. Performances of CFA piles were compared with those of a slurry displacement (SD) pile at the same site, thirteen pretensioned spun high-strength concrete (PHC) piles in the literature and empirical data in design code. Test results show that the ultimate bearing capacity of the CFA pile is highest among different pile types, and typically is twice that of the SD pile. Again, CFA pile produces the highest shaft resistances at 140 kPa and 153 kPa in two weathered granitic soils, while the base resistance of 3080 kPa is between those of the SD pile and the PHC pile. By field excavation, the superior mechanism of the CFA pile is suggested to avoid the formation of in-between bentonite layers and prevent preferential baseflow along fissures, both of which can weaken the soil–pile interface. Overall, this study provides fundamental data through UWFBG and explanations based on field observations which underpin the need for developing a design code specified for CFA piles in South China.

MsCNN-LSTM perimeter intrusion vibration signal identification method based on ultra-weak FBG arrays

To improve the recognition rate of ultra-weak fiber Bragg grating (UWFBG) arrays in perimeter security monitoring, this paper proposes a msCNN-LSTM combinatorial model recognition method, which combines an improved multi-scale convolutional neural network (msCNN) and a long short-term memory neural network (LSTM) to identify intrusion behaviors more accurately by synchronously extracting the multi-scale structural features and time-dependent relationships of vibration signals. Using msCNN to cross-learn multi-layer features, the hidden information between features at different scales can be obtained. The attention mechanism was applied to recalibrate the combined features extracted from the shallower layers, which enhanced the expression ability of features. Finally, the time dependence is analyzed by LSTM. Experimental results show that the scheme can effectively distinguish five typical intrusion behaviors, and the average recognition rate can reach 97.84%.

Sound source direction-of-arrival estimation method for microphone array based on ultra-weak fiber Bragg grating distributed acoustic sensor.

A sound source direction-of-arrival (DOA) estimation method for microphone array based on ultra-weak fiber Bragg grating (UW-FBG) distributed acoustic sensor is proposed. The principle of acoustic signal demodulation is introduced, the sound pressure sensitivity and frequency response range of a single UW-FBG microphone are analyzed, and a series linear array with three UW-FBG microphones is designed. Combined with convolutional recurrent neural networks, the DOA estimation method is developed. Log-Mel spectral features and SCOT/PHAT joint weighting generalized cross correlation features are used for DOA estimation. The corresponding system is established and experimentally verified. Results show that the measured sound pressure sensitivity of the UW-FBG microphone is in the range of 0.1032-3.306 rad/Pa within the frequency range of 1000-3000 Hz, and the peak sound pressure sensitivity is about 3.306 rad/Pa. The estimated mean error of 2D DOA estimation is about 2.85°, and the error of 3D DOA estimation is about 5.465°. This method has good application prospects in distributed sound source localization.

Separating method for multi-source vibration signals in ultra-weak fiber Bragg grating distributed acoustic sensors

Mixing multi-source vibration signals presents a major challenge in target recognition in distributed fiber optic acoustic sensor systems. This article proposes a deep learning method for separating ultra-weak fiber Bragg grating (UW-FBG) distributed acoustic sensor (DAS) multi-source signals. An experimental setup is constructed to create vibration environments for knocking, fan vibration, and steel pipe friction. The UW-FBG DAS system is used to collect single-source vibration signals, as well as two-source and three-source mixed vibration signals. First, a wavelet denoising algorithm is used to preprocess the data. Second, the datasets are constructed for simulated mixed signals and measured mixed signals. The Conv-TasNet deep learning network is utilized to separate DAS vibration signals. Results demonstrate that the proposed method can separate mixed signals of vibration, fan, and steel pipe friction sources, obtaining the corresponding time–frequency entropy similarity of 97.5%, 92.8% and 90.2%, and compared with commonly used multi-source signal separation methods, it has advantages in separation performance and separation time, and exhibits good robustness in different signal environments. This finding the efficacy of the separation method and provides a valuable solution for DAS multi-source signal separation problems in complex environments.

Enhanced Quasi-Distributed Accelerometer Array Based on φ-OTDR and Ultraweak Fiber Bragg Grating

Enhanced Quasi-Distributed Accelerometer Array Based on φ-OTDR and Ultraweak Fiber Bragg Grating

A fast wavelength detection method based on OTDR and 1-DDCNN in series overlapping spectra

To improve the rapidity and reliability of the subway tunnel fire monitoring system and realize the monitoring of small fire sources, the method of regional subsection demodulation is used to group several adjacent nearly identical ultra-weak fiber Bragg gratings (UWFBGs) sensors into a group, and the 1-D dilated convolutional neural network (1-DDCNN) is used to demodulate the same group of highly overlapping sensing signals. The well-trained 1-DDCNN model can achieve extremely low signal demodulation error. The experiment shows that the UWFBG demodulation scheme proposed in this paper improves the detection accuracy of the subway tunnel fire monitoring system and shortens the detection time. The root-mean-square error (RMSE) of four highly overlapping peak wavelengths of sensing signals is less than 1.5 pm, and the average demodulation time is less than 30 ms.

Intrusion Monitoring Based on High Dimensional Random Matrix by Using Ultra-Weak Fiber Bragg Grating Array

In order to ensure that a perimeter security system can work effectively, a convenient and effective event detection algorithm has an important engineering significance. Given the above background, in this paper, we propose a high reliability intrusion event recognition method and vibration sensing system, based on ultra-weak fiber Bragg grating array, by using high dimensional random matrix. We obtain a high sensitivity optical interference signal by constructing a patch-matched optical interference system, then compose the demodulated interference signal into a high-dimensional random matrix. The statistical characteristics of the matrix for the Marcenko-Pastur (M-P) law and ring law are used to confirm the presence of intrusion events efficiently, which can reflect the limit spectrum distribution of the high-dimensional random matrix; meanwhile, the abnormal state quantity and moment are obtained. Further, the average spectral radius value is used to judge the fault cause. Field experimental results show that the proposed method can effectively obtain the correct monitoring data for the sensor array. By comparing the monitoring results of normal operation and crusher operation, we can detect the intrusion event in 4.5 s, and the accuracy rate can reach more than 90%, which verifies that the proposed high-dimensional random matrix analysis method can work properly, proving a practical engineering application prospect.

Life Cycle Monitoring of Offshore Steel Pipe Piles via UWFBG Wireless Sensor Network

Monitoring the stress and deformation state of offshore steel pipe piles is very necessary for complex ocean environments. However, challenges still remain in remotely capturing detailed data on offshore projects. Herein, an ultraweak fiber Bragg gratings- (UWFBGs-) based wireless sensor network was established to obtain the high-precision strain along the piles. The No. 3 offshore wind power safety monitoring project in South Shandong Peninsula, China, used such a fiber-optic monitoring system. From January 1 to June 1, 2022, strain profiles of three offshore steel pipe piles were measured in seconds with a 1 m spatial resolution and a 1 με strain resolution, which clearly show sea level and mud-water interfaces. To assess the pile’s operational condition, the displacement and rotational angle of the mud surface are further calculated. It is discovered that the horizontal deformation at sea has a positive correlation with the wind, waves, and current, meanwhile decreasing with depth. The UWFBG-wireless sensor network can realize dynamic monitoring and early warning, which is promising for the life cycle monitoring of offshore wind turbine steel pipe piles.

Dense ultraweak fiber Bragg grating temperature detection method based on minimal gating unit demodulation

To solve the problems encountered by fiber Bragg grating temperature sensing technology, such as slow response speed and low detection accuracy in small heat source monitoring, a dense ultraweak fiber Bragg grating (UWFBG) temperature detection method based on minimal gating unit (MGU) demodulation is proposed and experimentally demonstrated. This method utilizes a dense UWFBG array with a spatial distance of 10 cm for linear temperature sensing, which is approximate in size to the heat source. Field programmable gate array/advanced RISC machine-embedded circuitry implements data transfer, analog-to-digital conversion, and 1 GSPS high-speed sampling. The MGU network is trained using a mixture of real and simulated data for the demodulation of the central wavelength. Parameters, such as batch size and hidden units, are explored to optimize the demodulation speed and accuracy of the overlapping spectra. Results show that the UWFBG detection temperature method based on the MGU network can demodulate 1000 sets of wavelength data in 2.7 s and obtains a root mean square error of less than 0.923 pm, thereby achieving a fast response to heat sources of 10 cm size and a detection temperature accuracy better than 0.34 °C.

Low-Frequency Fiber Optic Hydrophone Based on Ultra-Weak Fiber Bragg Grating

A winding low-frequency hydrophone based on ultra-weak fiber Bragg grating was studied. Through analyzing the hydrophone principle and the sensitivity factors, such as the material, radius, and thickness of the elastic cylindrical shell, the probe structure was optimized. In addition, an adaptive filtering technique based on the least mean square algorithm was introduced to improve the signal-to-noise ratio of the system. A low-frequency hydrophone with a working depth of 100 m and a diameter of 15 mm was developed and validated using the moving-water-column method. Results showed that the average acoustic pressure sensitivity of the hydrophone in the range of 1-100 Hz was –144.836 dB (re rad/μPa), the acoustic pressure sensitivity at 1 Hz was up to –130.85 (dB re rad/μPa). After using LMS filtering algorithm, the maximum signal-to-noise ratio of hydrophone can be increased by 4.62 dB, and the minimum detection pressure is 1.69×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–4</sup> Pa/Hz <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> . This hydrophone with high sensitivity and signal-to-noise ratio provides a reference for low-frequency underwater detection.