Ultra-weak Fiber Bragg Gratings Research Articles

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.

The Impact of Rayleigh Scattering in UWFBG Array-Based Φ-OTDR and Its Suppression Method.

Ultra-weak fiber Bragg grating (UWFBG) array-based phase-sensitive optical time-domain reflectometry (Φ-OTDR) utilizes the interference interaction between the reference light and the reflected light from the broadband gratings for sensing. It significantly improves the performance of the distributed acoustic sensing system (DAS) because the intensity of the reflected signal is much higher than that of the Rayleigh backscattering. This paper shows that Rayleigh backscattering (RBS) has become one of the primary noise sources in the UWFBG array-based Φ-OTDR system. We reveal the impact of the Rayleigh backscattering signal on the intensity of the reflective signal and the precision of the demodulated signal, and we suggest reducing the pulse duration to improve the demodulation accuracy. Experimental results demonstrate that using light with a 100 ns pulse duration can improve the measurement precision by three times compared with the use of a 300 ns pulse duration.

Thermo-hydro-poro-mechanical responses of a reservoir-induced landslide tracked by high-resolution fiber optic sensing nerves

Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied. However, it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure. Here, we aim to investigate the subsurface multi-physics of reservoir landslides under two extreme hydrologic conditions (i.e. wet and dry), particularly within sliding masses. Based on ultra-weak fiber Bragg grating (UWFBG) technology, we employ special-purpose fiber optic sensing cables that can be implanted into boreholes as “nerves of the Earth” to collect data on soil temperature, water content, pore water pressure, and strain. The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring. These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide, with a resolution of 1 m except for the pressure sensor. We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole. Results show that wet years are more likely to motivate landslide motions than dry years. The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years. The dynamic groundwater table is located at depths of 9–15 m, where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles. These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability, allowing us to correlate them to local damage events and potential global destabilization. This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes, which may form the basis for a landslide monitoring and early warning system.

Vehicle identification using deep learning for expressway monitoring based on ultra-weak FBG arrays.

A deep learning with knowledge distillation scheme for lateral lane-level vehicle identification based on ultra-weak fiber Bragg grating (UWFBG) arrays is proposed. Firstly, the UWFBG arrays are laid underground in each expressway lane to obtain the vibration signals of vehicles. Then, three types of vehicle vibration signals (the vibration signal of a single vehicle, the accompanying vibration signal, and the vibration signal of laterally adjacent vehicles) are separately extracted by density-based spatial clustering of applications with noise (DBSCAN) to produce a sample library. Finally, a teacher model is designed with a residual neural network (ResNet) connected to a long short-term memory (LSTM), and a student model consisting of only one LSTM layer is trained by knowledge distillation (KD) to satisfy the real-time monitoring with high accuracy. Experimental demonstration verifies that the average identification rate of the student model with KD is 95% with good real-time capability. By comparison tests with other models, the proposed scheme shows a solid performance in the integrated evaluation for vehicle identification.

Distributed dynamic strain measurement with a direct detection scheme by using a three-step-phase-shifted double pulse in a UWFBG array.

We demonstrate a stable homodyne phase demodulation method with a double pulse based on an ultra-weak fiber Bragg grating (UWFBG) array. The technique divides one of the probe pulses into three sections and introduces successive 2π/3 phase differences into each section. By using a simple direct detection scheme, it can achieve distributed and quantitative vibration measurement along the UWFBG array. Compared to the traditional homodyne demodulation technique, the proposed technique is more stable and easier to accomplish. Moreover, the reflected light from the UWFBGs can provide a signal that is modulated uniformly by the dynamic strain and multiple results for averaging, resulting in a higher signal-to-noise ratio (SNR). We experimentally demonstrate the technique's effectiveness by monitoring different vibrations. The SNR for measuring a generic 100 Hz, 0.08 rad vibration in a 3 km UWFBG array with a reflectivity of -40 to -45 dB is estimated to be ∼44.92 dB.

Anti-noise UWFBG-array enhanced DAS system using double-pulse-based time-domain adaptive delay interference.

An anti-noise interrogation technique for ultra-weak fiber Bragg grating (UWFBG)-based distributed acoustic sensing (DAS) systems is proposed and demonstrated using double-pulse-based time-domain adaptive delay interference. This technique breaks the limitation that the optical path difference (OPD) between the two arms of the interferometer should be completely matched with the entire OPD between the adjacent gratings in the traditional single-pulse system. The length of the delay fiber in the interferometer can be reduced, and the double-pulse interval can adapt flexibly to the UWFBG array with different grating spacing. The acoustic signal is restored accurately when the grating spacing is 15 m or 20 m by the time-domain adjustable delay interference. Moreover, the noise induced by the interferometer can be suppressed significantly as compared to using a single pulse, and above 8-dB signal-to-noise ratio (SNR) enhancement can be obtained without any extra optical devices when the noise frequency and the vibration acceleration are below 100 Hz and 0.1 m/s2, respectively.

Evaluating the effect of post-grouting on long bored pile based on ultra-weak fiber Bragg grating array

Fiber optic sensing has been widely acknowledged as a dependable tool to monitor pile performance. However, its applicability is restricted by the limited multiplexing capacity of fiber Bragg gratings (FBG) or the high equipment cost and poor real-time performance of distributed fiber optic sensing systems. In this paper, a novel sensing cable and system were developed based on ultra-weak FBG array for strain measurement of large-scale engineering structures. Through the laboratory calibration and field test of a post-grouting pile, the good strain measurement performance of the sensing cable and system were confirmed. Based on the distributed strain data obtained by the UWFBG array, the strengthening mechanism of the pile was well explained. The UWFBG array and sensing system could realize distributed, high-precision, and real-time strain measurement with a spatial resolution of 1 m within 1.5 km, which has great potentials in the monitoring of geo-environment and engineering structures.

Ultra-weak FBG sensing for identification and analysis of plastic zone of soil caused by supported excavation

Deformation caused by supported excavation makes significant impact on foundation pit stability. Evaluation of potential plastic zone is crucial for early warning of foundation pit instability. To address the shortcomings of existing evaluation methods, a new identification method based on the ultra-weak fiber Bragg grating (UWFBG) technology was proposed. To validate this approach, a UWFBG monitoring program was implemented in the boreholes as part of the Shanghai Metro Line 18 project. The monitoring results were utilized to determine the range of plastic zone for each borehole. It was noted that there are two plastic deformation zones formed with different mechanisms, one located near the base of the diaphragm wall (DW) and the other is distributed within the range of 6–19 m below the excavation face. Lastly, a criterion of early warning of foundation pit instability was presented. By analyzing the monitoring data, the highest risk level of this project was determined as “Attention”, implying the necessity of taking measures to prevent the local plastic zone from evolving into a full-scale sliding surface.