Distributed Vibration Sensor Research Articles

Deep Integration of Fiber-Optic Communication and Sensing Systems Using Forward-Transmission Distributed Vibration Sensing and on-off Keying.

The deep integration of communication and sensing technology in fiber-optic systems has been highly sought after in recent years, with the aim of rapid and cost-effective large-scale upgrading of existing communication cables in order to monitor ocean activities. As a proof-of-concept demonstration, a high-degree of compatibility was shown between forward-transmission distributed fiber-optic vibration sensing and an on-off keying (OOK)-based communication system. This type of deep integration allows distributed sensing to utilize the optical fiber communication cable, wavelength channel, optical signal and demodulation receiver. The addition of distributed sensing functionality does not have an impact on the communication performance, as sensing involves no hardware changes and does not occupy any bandwidth; instead, it non-intrusively analyzes inherent vibration-induced noise in the data transmitted. Likewise, the transmission of communication data does not affect the sensing performance. For data transmission, 150 Mb/s was demonstrated with a BER of 2.8 × 10-7 and a QdB of 14.1. For vibration sensing, the forward-transmission method offers distance, time, frequency, intensity and phase-resolved monitoring. The limit of detection (LoD) is 8.3 pε/Hz1/2 at 1 kHz. The single-span sensing distance is 101.3 km (no optical amplification), with a spatial resolution of 0.08 m, and positioning accuracy can be as low as 10.1 m. No data averaging was performed during signal processing. The vibration frequency range tested is 10-1000 Hz.

A hand-crafted φ-OTDR event recognition method based on space-temporal graph and morphological object detection

In the long-range pipeline monitoring application of Fiber Optic Distributed Vibration Sensor (FODVS), instantaneous vibration event with high energy generates a pair of invert-V signature in the space-temporal graph, providing clear characteristic for event recognition. This paper proposed an event recognition method utilizing FODVS and hand-crafted morphological object detection algorithm. Phase-sensitive Optical Time Domain Reflectometer (φ-OTDR) was employed as the distributed sensor to collect the real field space-temporal graph data containing invert-V signature. Bandpass filter and Gaussian blur were utilized to suppress random noise and enhance the invert-V signature, then a hand-crafted morphological object detection method was proposed to recognize the invert-V pattern. The proposed method was proved to be able to accurately capture the invert-V signature in the space-temporal graph. This paper provided a concept-proof preliminary demonstration on the hand-crafted FODVS event recognition scheme for pursuing model with good interpretability and strong adaptability to varied environments.

Multi-point vibration positioning method for long-distance forward transmission distributed vibration sensing

Observation of intensity, phase, or polarization properties of light propagating through telecom submarine cables can enable widespread monitoring of geological and undersea events, such as earthquakes, tsunamis, and shipping lane traffic. We conducted a comparative analysis of external physical perturbations acting on submarine optical cables and unprotected optical fibers; introduced both intensity and phase demodulation-based sensing systems for long-distance vibration sensing; presented an extension to the phase-spectrum time delay method for forward-transmission distributed sensing (same as optical communications) to distinguish and quantify multiple simultaneous vibration events; and overcame the previous spatial resolution fundamental lower limit set by the time-domain sampling rate. We experimentally demonstrated multi-vibration positioning over 202.3 km single-span sensing distance, with a positioning accuracy as small as 17.9 m for sinewave vibrations, and a spatial resolution of 1.25 m. Other key sensor parameters include phase sensitivity of 40.6 mrad/µε @ 80 Hz, a corresponding limit of detection (LoD) of 101.7 pε/Hz1/2, intensity sensitivity of 7.1%/µε @ 80 Hz, and a corresponding LoD of 20.1 pε/Hz1/2. The tested frequency range was 0.01-100 Hz. No signal averaging was performed during signal processing to allow faster real-time processing, which would otherwise further improve the results. This forward transmission approach has the potential to upgrade the existing submerged global internet fiber-optic network into a vast ocean-spanning observation network while allowing telecom operations to operate normally without sacrificing bandwidth.

Adaptive block-matching and 4D denoising scheme for a distributed vibration sensing system.

A noise reduction method based on the block-matching and 4D (BM4D) scheme is proposed to improve the signal-to-noise ratio (SNR) in distributed vibration sensing (DVS) systems. In the proposed scheme, the original Rayleigh backscattering (RBS) signal is converted into a three-dimensional image containing Rayleigh trajectory and energy information. The correlation between the time-domain and spatial-domain signals is then used to achieve the denoising operation. An experimental demonstration containing both one and two vibration points is conducted to verify the effectiveness of the proposed denoising scheme. The experimental results show that the BM4D scheme can provide higher SNR improvement than the current normalized least mean square (NLM), empirical mode decomposition combined with time-frequency peak filtering (EMD-TFPF), and BM3D schemes. Based on the BM4D scheme, the SNR is improved from 1.27 dB to 12.84 dB in the condition of one vibration point and from 6.23 dB to 20.14 dB in the condition of two vibration points. It is also indicated that the high-frequency noise of the vibration waveform after the denoising operation is mitigated by more than 30 dB, showing the potential for applications of accurate waveform characterization in cost-effective DVS systems.

Vectorial Distributed Vibration Sensing Based on Two-Beam $\phi$-OTDR and Four-Core Fiber

Vectorial Distributed Vibration Sensing Based on Two-Beam $\phi$-OTDR and Four-Core Fiber

Localization Enhancement of Forward-Transmission Distributed Vibration Sensors Using Phase Differentiation Endpoint Amplification

Localization Enhancement of Forward-Transmission Distributed Vibration Sensors Using Phase Differentiation Endpoint Amplification

Direct Detection With High Spatial Resolution Based on Distributed Vibration Sensor by Precise Signal Delay

Direct Detection With High Spatial Resolution Based on Distributed Vibration Sensor by Precise Signal Delay

150 km Single-span Distributed Vibration Sensor based on Compensated Self-interference Forward Transmission

150 km Single-span Distributed Vibration Sensor based on Compensated Self-interference Forward Transmission

Super-long-range distributed vibration sensor based on the polarimetric forward-transmission of light.

Undersea earthquake-triggered giant tsunamis pose significant threats to coastal areas, spanning thousands of kilometers and affecting populations, ecosystems, and infrastructure. To mitigate their impact, monitoring seismic activity in underwater environments is crucial. In this study, we propose a new, to the best of our knowledge, approach for monitoring vibrations in submarine optical cables. By detecting vibration-induced polarization rotation, our dual-wavelength fiber-optic sensing system enables precise measurement of acoustic/vibration amplitude, frequency, and position. As a proof of concept, a double-ended forward-transmission distributed fiber-optic vibration sensor was demonstrated with a single vibration source with a sensitivity of 3.4 mrad/µε at 100 Hz (20 m fiber on PZT), limit of detection of 1.7 pε/Hz1/2 at 100 Hz, sensing range of 121.5 km without an optical amplifier, spatial resolution of 5 m, and position error as small as 34 m. The vibration frequency range tested is from 0.01 to 100 Hz. The sensing system has several advantages, including elegant setup, noise mitigation, and super-long sensing distance.

An intelligent crash recognition method based on 1DResNet-SVM with distributed vibration sensors

The vibration recognition along the fiber is still a challenge in highway accident monitoring with distributed optical fiber vibration sensing system (DVS). In this paper, a method named 1DResNet-SVM combining 1D residual Neural Network (1DResNet) and support vector machines (SVM) is proposed. One-dimensional raw vibration signals are used to avoid information loss of data. One-dimensional convolution is combined with residual block to solve the degradation of deep network models. Experiments show that the proposed 1DResNet can extract the distinguishable characteristics of DVS vibration signals better than traditional manual feature extraction methods. Then SVM is selected to replace the fully-connected layer in the network which can effectively recognize crash. The proposed 1DResNet-SVM model can achieve an average recognition rate of 96.2%. Three typical events in highway accidents are carried out in field tests, which can reach 100% especially for crash recognition. This paper provides a new method for the identification of crash accidents.

Distributed vibration sensor with a high strain dynamic range by harmonics analysis

Distributed vibration sensors (DVSs) have important applications in industrial production. A large strain dynamic range is very important for DVSs, but difficult to achieve as it often requires a complex sensing system or cumbersome data processing. This study shows that the strain dynamic range of the DVS can be improved by analyzing the harmonic numbers in the vibration response spectrum, and the vibration amplitude can be quantitatively measured with a strain resolution of 0.78 με for each additional harmonic. The system and data analysis method can significantly improve the strain dynamic range in comparison with traditional DVS based on the intensity or polarization information.

Railway Intrusion Events Classification and Location Based on Deep Learning in Distributed Vibration Sensing

With the rapid development of the high-speed railway industry, the safety of railway operations is becoming increasingly important. As a symmetrical structure, traditional manual patrol and camera surveillance solutions on both sides of the railway require enormous manpower and material resources and are highly susceptible to weather and electromagnetic interference. In contrast, a distributed fiber optic vibration sensing system can be continuously monitored and is not affected by electromagnetic interference to false alarms. However, it is still a challenge to identify the type of intrusion event along the fiber optic cable. In this paper, a railway intrusion event classification and location scheme based on a distributed vibration sensing system was proposed. In order to improve the accuracy and reliability of the recognition, a 1 DSE-ResNeXt+SVM method was demonstrated. Squeeze-and-excitation blocks with attention mechanisms increased the classification ability by sifting through feature information without being influenced by non-critical information, while a support vector machine classifier can further improve the classification accuracy. The method achieved an accuracy of 96.0% for the identification of railway intrusion events with the field experiments. It illustrates that the proposed scheme can significantly improve the safety of railway operations and reduce the loss of personnel and property safety.

Optical fiber distributed vibration sensor based on dual Mach-Zehnder interferometer using an improved phase generated carrier algorithm

Optical fiber distributed vibration sensor based on dual Mach-Zehnder interferometer (DMZI) has been extensively studied for vibration detections. Whereas the accurate signal demodulation technique is still a challenging task for some particular detections, especially for those dynamic vibration sensing with high frequency. In this work, we propose an ameliorated phase generated carrier (PGC) demodulation algorithm for the dynamic vibration sensing in the DMZI based vibration sensing system. A piezoelectric stretcher is firstly cascaded with the reference arm of the DMZI structure. Then a dynamic strain would be produced to change the phase difference of the DMZI structure when the carrier signal and the measured vibration signal are applied on the stretcher and the sensing arm of the DMZI, respectively. Finally, the applied vibration signal can be accurately demodulated by using the improved PGC demodulation algorithm. Theoretical analysis and derivation of the proposed vibration sensing approach are given in detail. Simulation analysis and vibration experiments with different vibration patterns have been implemented to demonstrate the effectiveness of the proposed sensing approach. Experimental results show that the relation between the applied signal and the demodulated signal is approximately linear. Moreover, the high-order harmonic effect and the intensity fluctuation of the demodulated signals have been effectively suppressed in the proposed DMZI based vibration sensing system.

A Novel Distributed Vibration Sensor Based on Fading Noise Reduction in Multi-Mode Fiber.

Multi-mode fiber (MMF) is used in a polarization-sensitive optical time domain reflectometer (OTDR) for vibration event location and spectrum analysis. The vibration events acting on MMF are considered to be the optical polarization state and phase diversifying process for fading noise reduction. In addition, data averaging with continuous positions and the fast Fourier transform (FFT) method is proposed to extract the spectrum of the vibration events. In the experiment, the vibration events are loaded at the positions of 5.167 and 10.145 km, respectively, along MMF. The experimental results demonstrate that the vibration event can effectively diversify the optical polarization state and phase of the Rayleigh scattering light to make the averaged OTDR trace behind the vibration position converge rapidly, which helps to locate corresponding vibration events and extract the vibration spectrum. It is inferred that the new distributed vibration sensor shall have a lower false alarm rate, as it can greatly reduce the errors caused by randomness of the sensing light signals. Additionally, it also saves time in comparison with the method that analyzes the vibration spectra for all the positions along the fiber under test.

Distributed vibration sensor with a lasing phase-sensitive OTDR.

The authors experimentally demonstrate the operation of a lasing phase-sensitive optical time-domain reflectometer (Φ-OTDR) based on random feedback from a sensing fiber. Here, the full output of the laser provides the sensing signal, in contrast to the small backscattered signal measured in a conventional OTDR. In this proof-of-principle demonstration, the laser operates as a distributed vibration sensor with signal-to-noise ratio of 23-dB and 1.37-m spatial resolution.

Unsupervised learning method for events identification in φ-OTDR

In this paper, an unsupervised-learning method for events-identification in φ-OTDR fiber-optic distributed vibration sensor is proposed. The different vibration-events including blowing, raining, direct and indirect hitting, and noise-induced false vibration are clustered by the k-means algorithm. The equivalent classification accuracy of 99.4% has been obtained, compared with the actual classes of vibration-events in the experiment. With the cluster-number of 3, the maximal Calinski-Harabaz index and Silhouette coefficient are obtained as 2653 and 0.7206, respectively. It is found that our clustering method is effective for the events-identification of φ-OTDR without any prior labels, which provides an interesting application of unsupervised-learning in self-classification of vibration-events for φ-OTDR.

Quantum Key Distribution over 658km Fiber with Distributed Vibration Sensing.

Twin-field quantum key distribution (TFQKD) promises ultralong secure key distribution which surpasses the rate distance limit and can reduce the number of the trusted nodes in long-haul quantum network. Tremendous efforts have been made toward implementation of TFQKD, among which, the secure key with finite size analysis can distribute more than 500km in the lab and in the field. Here, we demonstrate the sending-or-not-sending TFQKD experimentally, achieving a secure key distribution with finite size analysis over a 658km ultra-low-loss optical fiber. Meanwhile, in a TFQKD system, any phase fluctuation due to temperature variation and ambient variation during the channel must be recorded and compensated, and all this phase information can then be utilized to sense the channel vibration perturbations. With our quantum key distribution system, we recovered the external vibrational perturbations generated by artificial vibroseis on both the quantum and frequency calibration link, and successfully located the perturbation position in the frequency calibration fiber with a resolution better than 1km. Our results not only set a new distance record of quantum key distribution, but also demonstrate that the redundant information of TFQKD can be used for remote sensing of the channel vibration, which can find applications in earthquake detection and landslide monitoring besides secure communication.

Distributed vibration sensor based on mode coupling in weakly coupled few-mode fibers.

In recent years, optical fiber distributed vibration sensors (DVSs) have received extensive investigation and play a significant role in different applications, such as structural health monitoring. In this Letter, we propose for the first time, to the best of our knowledge, a DVS mechanism based on linearly polarized mode coupling in weakly coupled few-mode fibers (FMFs), in which dynamic transverse stress induced by external vibration is measured with quantifiable and spatially resolvable mode coupling along the sensing FMF with ultralow inherent modal crosstalk. A swept-wavelength interferometer method is implemented and the involved data processing method is designed. A proof-of-concept DVS system is established and 5 Hz to 49 kHz frequency response, -50 dB detection sensitivity, and 22 m spatial resolution are successfully demonstrated based on a 9.6 km weakly coupled two-mode fiber. The wide frequency response over a long sensing length for the proposed scheme may extend the application range of DVS systems.

Markov Transition Fields and Deep Learning-Based Event-Classification and Vibration-Frequency Measurement for φ-OTDR

In this paper, a novel method, relying on Markov Transition Fields (MTF) and deep learning, is proposed to classify the vibration-events and measure vibration-frequency, for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {\varphi } $ </tex-math></inline-formula> -OTDR based fiber-optic distributed vibration sensor. The normalized time series of a signal detected by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varphi }$ </tex-math></inline-formula> -OTDR are converted into an image of MTF, serving as a sample for supervised learning. Next, the MTF image is classified using a convolutional neural network (CNN) and a fully connected neural network. Initially, five different vibration-events including blowing, rain, direct and indirect knocking, and false vibration caused by noises, are classified. Furthermore, approximate single-frequency vibrations (with different central-frequency) are regarded as different classifications, where vibration frequency can be obtained by classification tasks. Compared to conventional method based on phase-demodulation with complex techniques and high cost, the proposed method demonstrates a low-cost and fast-time method. Moreover, learning algorithm is trained and tested through the data sets generated by experiments. To evaluate feasibility and validate the performance of classification, we analyze the test accuracy of classification and relevant receiver operating characteristic curves. The results indicate that our method is effective for the classification of both vibration-events and single-frequency vibrations. We believe that this work provides not only a technical reference for the application of deep learning in measuring the vibration frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varphi } $ </tex-math></inline-formula> -OTDR using software-technique, but also an impressive inspiration for event-classification of vibrations by a combination of image-processing methods.

Sampling Rate Enhancement and Fading Suppression of Φ-OTDR With Frequency Division Multiplex Technique

To capture vibration waveforms whose frequencies exceed the limitation set by sensing fiber length in Φ–OTDR distributed vibration sensing (DVS), we propose a sampling rate enhancement technique based on frequency-division-multiplexing (FDM). Our proposal, unlike conventional FDM-based sampling rate enhancement methods, directly eliminates inherent distortion by using complementary frequency concept with new probe pulse sequence. The proposed method can suppress the distortion of arbitrary waveforms in regular fiber without prior knowledge of the vibration. The proposal can accurately measure vibration waveforms and fully characterize the vibration on a standard single-mode fiber; it determines not only the frequency but also the correct amplitude and the exact shape of the vibration. Furthermore, our distortion compensation scheme incorporates a state-of-the-art FDM-based fading suppression technique, yielding high sampling rates and high sensitivity simultaneously. We demonstrate the validity of the proposal by proof-of-concept experiments in which the number of multiplexed frequencies is set to three for both sampling rate enhancement and fading suppression. In creating the experimental setup, we take the great care with the optical pulse shape and the carrier frequencies to avoid crosstalk. We successfully measure vibration waveforms with frequencies ranging from 1 kHz to 9 kHz on a sensing fiber with length of about 12 km.