Optical Time Domain Reflectometer Research Articles

Interference fading suppression with a multi-subcarrier pulse in a distributed acoustic sensor

A low-complexity multi-subcarrier pulse generation scheme is proposed to suppress the interference fading in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) based distributed acoustic sensor (DAS) with heterodyne coherent detection. The multi-subcarrier pulse is generated in the digital domain based on the proper clipping operation of a sine signal. The localization and recovery of the disturbance signal are realized by the spectrum extraction and rotated vector sum (SERVS) method. The experimental results show that the occurrences of interference fading can be significantly reduced. The intensity fluctuation is reduced from ∼75 dB to ∼25 dB. Multiple disturbance signals are successfully demodulated to verify the effectiveness of the proposed method.

Dead Zone Fault Detection Optimization Method for Few-Mode Fiber Links Based on Unexcited Coupled Higher-Order Modes

Dead Zone Fault Detection Optimization Method for Few-Mode Fiber Links Based on Unexcited Coupled Higher-Order Modes

Interference fading suppression with fault-tolerant Kalman filter in phase-sensitive OTDR

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10 Hz to 2400 Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6 Hz and an average error of 7.6 Hz are obtained. The demodulation frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25 Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance.

ANALISIS GANGGUAN DAN IDENTIFIKASI KABEL FIBER OPTIC MENGGUNAKAN OTDR DI OTB CIREBON-BREBES R4

Fiber optic cables that experience interference and breaks are a common problem in modern communications infrastructure. Interference and breaks in optical cables can disrupt data transmission between locations and cause a decrease in signal quality. The use of an Optical Time Domain Reflectometer (OTDR) has become a common method for detecting faults in optical cables by displaying the attenuation of the cable under test. Although optical fiber has a large capacity and high speed for transmitting information, relatively small attenuation still has a significant impact on transmission quality. The importance of maintaining and handling this interference is because attenuation can cause power loss in fiber optic transmission, hamper data transmission, and affect the quality of internet services

Enhanced-Reach Polarization Optical Time-Domain Reflectometry Technique for Fibre Optical Infrastructure Monitoring

Abstract This interdisciplinary research is focused on constructing and demonstrating enhanced-reach polarization optical time-domain reflectometer (POTDR) for monitoring single-mode fibre optical communication lines. An optical signal state of polarization measurements enables several new possibilities for real-time monitoring solutions in fibre optics. However, there is no commercial equipment available to determine the location in fibre optical cable where the light polarization state changes. The authors present a monitoring technique of an optical signal state of polarization based on reflected signal time-amplitude analysis with improved operation parameters, namely, timing resolution (2 to 3 ps RMS) and amplitude measurement resolution of nanosecond scale pulses (8 to 10 bits) for monitoring of fibre optical communication lines. Additionally, the demonstrated POTDR provides a considerable optical fibre line measurement reach of up to 40 km.

Rayleigh and Brillouin self-heterodyne detection Brillouin optical time domain reflectometer system based on phase shift keying pulse encoding

Rayleigh and Brillouin self-heterodyne detection Brillouin optical time domain reflectometer system based on phase shift keying pulse encoding

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network

The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning-based fault diagnosis algorithms to extract these minute features. In addition, the OTDR curve field fault data are scarce, and data-driven deep neural network that needs a lot of data training cannot meet the requirements. In response to this issue, this paper proposes a communication fiber state diagnosis model based on fuzzy clustering and an improved ResNet. First, the pretrained residual network (ResNet) is modified by removing the classification layer and retaining the feature extraction layers. A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. During the diagnosis process, the OTDR curves are input into the network, resulting in 512 features outputted in the GAP layer. These features are used to construct a feature vector matrix, and a dynamic clustering graph is formed using fuzzy clustering to realize the fiber state diagnosis. Through on-site data detection and validation, it has been demonstrated that the improved ResNet can effectively identify the full cycle of fiber states.

Brillouin frequency shift extraction by bidirectional integration of Brillouin gain spectrum

A novel Brillouin frequency shift (BFS) extraction approach based on bidirectional integration is proposed and investigated for mitigating the fluctuations in Brillouin optical time domain reflectometer (BOTDR) system. The proposed method involves calculating the BFS through the forward and backward integrations of the original Brillouin gain spectrum (BGS) data. The findings from both simulations and experiments demonstrate that bidirectional integration achieves superior BFS accuracy compared to Lorentzian curve fitting. To further evaluate its performance, the root mean square error of BFS is measured under the different signal-to-noise ratios (SNRs) and frequency steps and the results show that bidirectional integration has higher tolerance for low SNR. Specifically, bidirectional integration achieves a BFS accuracy of 0.853 MHz under SNR of 5.22 dB and significantly reduces the measurement time by 85.86 %. Additionally, the introduced coefficient k characterizes the influence on the BGS compression, with bidirectional integration exhibiting a narrower full width at half maximum of BGS (52.74 MHz) compared to Lorentzian curve fitting (67.45 MHz).

Interference fading suppression for distributed acoustic sensor using frequency-shifted delay loop

In order to suppress the interference fading of phase-sensitive optical time domain reflectometer (Φ-OTDR), a multi-frequency detection method based on frequency-shifted delay loop (FSDL) is proposed. A probe pulse train with up to five consistent frequency components are generated by periodic frequency-shifted, amplification, delay and filtering processing in FSDL. The piecewise aggregate approximation (PAA) is introduced to compress the amount of operational data. Wavelet energy spectrum analysis and support vector machine (SVM) are used to extract features and realize the fading classification. With the help of fading label making and restoration, the multi-frequency signals are intelligently aggregated using rotated-vector-sum (RVS). Experimental results show that PAA has applicability in data compression of beat signals. After 5-layer wavelet energy spectrum analysis, the fading binary classification accuracy can reach 96.77 %, and the fading signals identified after segmentation can be restored to the original data. The fading probability based on SVM output labels can be reduced to 1.89 %. The SVM-based classification output labels aggregation shows that the vibration positioning signal-to-noise ratio (SNR) can be increased to 13.52 dB, the phase demodulation curve is smoother, and the demodulation SNR can be up to 17.63 dB. Finally, the R-Square of 0.997 for the amplitude of demodulated vibration signal in repeatability experiment verifies the validity of this effective scheme for fading recognition and suppression.

Highly discriminative and adaptive feature extraction method based on NMF–MFCC for event recognition of Φ-OTDR

To enhance the capability of phase-sensitive optical time domain reflectometers (Φ-OTDR) to recognize disturbance events, an improved adaptive feature extraction method based on NMF–MFCC is proposed, which replaces the fixed filter bank used in the traditional method to extract the mel-frequency cepstral coefficient (MFCC) features by a spectral structure obtained from the Φ-OTDR signal spectrum using nonnegative matrix factorization (NMF). Three typical events on fences are set as recognition targets in our experiments, and the results show that the NMF–MFCC features have higher distinguishability, with the corresponding recognition accuracy reaching 98.47%, which is 7% higher than that using the traditional MFCC features.

Fading suppression in the Ф-OTDR system based on a phase-modulated optical frequency comb.

In this paper, what we believe to be a novel method is proposed to suppress the fading effect of the phase-sensitive optical time domain reflectometer (Ф-OTDR) by using a phase-modulated optical frequency comb. In the Ф-OTDR system, intensity distributions of Rayleigh backscattering (RBS) light are different for pulsed probe lights with different central frequencies, therefore the locations of the fading points corresponding to signals of different frequencies are differently distributed, allowing the use of frequency division multiplexing to suppress the fading effects. In the experimental system of this paper, a continuous light in the form of a frequency comb is firstly generated through phase modulation. It is then modulated into a pulsed probe light and injected into the sensing fiber to produce different RBS intensity distributions. Finally, the extracted phase is processed by using the amplitude evaluation method, so that the distorted phase can be eliminated. Fading suppression is achieved using our system, and the effect of suppression is evaluated. By using an equal-amplitude optical frequency comb containing seven frequency components, the fading probability density of the system is dramatically reduced from the range of 5.49%-9.83% to 0.08%. Compared with the conventional system using a single acoustic-optic modulator to generate the frequency shift, the method proposed in this paper features a larger modulation bandwidth and more flexible frequency combination scheme to better suppress the fading effect. This method does not sacrifice the response bandwidth of the system, and the phase delay can be precisely controlled, which helps to fully suppress the fading effect.

Multi-step-index fiber with a large number of weakly coupled OAM mode groups for IM/DD systems in data centers: design, fabrication, and characterization

Mode division multiplexing (MDM) technique based on weakly coupled few-mode fibers (FMF) is promising to enhance the capacity of short-reach transmission. We design and fabricate a multi-step-index FMF (MSIF), which supports weakly coupled first-order radial orbital angular momentum mode group (OAMl,1 MG) for MDM transmission. We use three layers of core to regulate the minimum effective refractive index difference (min|Δn eff |) between OAMl,1 MG and the adjacent MGs. In experiments, we demonstrate that the fabricated MSIF can support up to OAM6,1 with the interferometric method, and the loss measured by an optical time-domain reflectometer (OTDR) can achieve <0.5 dB/km for the OAMl,1 with an order from |l| = 0 to |l| = 6. The inter-mode-group cross talk (XT) is tested by the power measurement, and the system-level XT after 20 km fiber transmission in the worst case is about −11.1 dB.

Characterization of Laser Frequency Stability by Using Phase-Sensitive Optical Time-Domain Reflectometry

A new method to measure laser phase noise and frequency stability based on the phase-sensitive optical time-domain reflectometry is proposed. In this method, the laser under test is utilized in a phase-sensitive optical time-domain reflectometer, which employs phase-modulated dual pulses and acts as an optical frequency discriminator: laser frequency fluctuations are deduced from the analysis of the reflectometer data corresponded to phase fluctuations along the vibration-damped and thermally insulated fiber spool. The measurement results were validated by comparison with direct optical heterodyning of the tested and more coherent reference lasers. The use of dual pulses generated by an acousto-optic modulator makes it easy to adjust the time delay during measurements, which distinguishes favorably the proposed method from standard optical frequency discriminators. The method is suitable for testing highly coherent lasers and qualifying their parameters such as linear drift rate, random frequency walk rate, white frequency noise (which is directly related to laser instantaneous linewidth), and flicker noise level.

Multi-Event Location Denoising Scheme for φ-OTDR Based on FFDNet Network

In order to improve the signal-to-noise ratio (SNR) of vibration sensing in the phase-sensitive optical time-domain reflectometer (φ-OTDR) system, a fiber sensing signal processing method based on the FFDNet convolutional neural network is proposed in this paper. In the network, the concept of residual learning is introduced, which involves constructing a residual mapping and utilizing multi-layer convolutional neural networks to learn the noise distribution present in the original image. The denoised result can be obtained by subtracting the learned noise from the original image. We have built a φ-OTDR system based on coherent detection, using three PZTs as simulated vibration sources and a series of experiments at 200 Hz, with each experiment simulating a single vibration event or multiple vibration events by setting different intensities. The experimental results demonstrate that the FFDNet based fiber optic sensing signal processing method enhances the SNR to 37.84 dB, 37.11 dB, and 37.31 dB, respectively, while preserving vibration signal details more effectively than wavelet denoising and Gaussian filtering techniques. The trained FFDNet model has great potential for improving the performance of the φ-OTDR system and has some practical application value.

Modulation instability in long distance φ-OTDR system

Recent trends in long distance phase-sensitive optical time-domain reflectometer (φ-OTDR) have led to a proliferation of studies on modulation instability (MI). However, most of these studies overlook the inaccuracies of MI gain spectrum based on the linear stability analysis of the nonlinear Schrödinger equation in the case of long distance over about 10 km. Here, we conducted a comprehensive exploration of MI characteristics in the φ-OTDR system, including the effects of input power, detection distance, and background noise on the MI evolution process. Experiments confirmed the presence of an intrinsic spectral component of MI surrounding the signal in long-distance detection, which has not been reported previously. Further, we proposed a fitting expression using numerical simulation to determine the optimal input power for the φ-OTDR system. Compared to traditional threshold formulas, it does not require complex theoretical derivations and demonstrated significantly higher accuracy, particularly in systems with ultra-long detection distances. The deviation was less than 10%, demonstrating its potential as a more precise and simplified method of determining optimal input power. This could help improve the efficiency and reliability of these systems, leading to further advancements in the field of MI research.

A Distributed Acoustic Sensor with a 120-km Sensing Range Based on a Phase-Sensitive Optical Time-Domain Reflectometer and a Remotely Pumped Erbium-Doped Fiber Amplifier

A Distributed Acoustic Sensor with a 120-km Sensing Range Based on a Phase-Sensitive Optical Time-Domain Reflectometer and a Remotely Pumped Erbium-Doped Fiber Amplifier

Threshold design in fiber termination identification using local injection and OTDR

We study how to set a threshold for how to identify the end of an optical fiber by reflectance ratio between wavelengths calculated from measurements of a temporary optical coupler (TOC) and a two-wavelength Optical Time Domain Reflectometer (OTDR). The threshold is affected by the difference in propagation loss between the wavelengths from the OTDR to the end of the optical fiber. The effectiveness of the threshold is inferred from measurements of the reflectance ratio of an Optical Network Unit (ONU) used for a Passive Optical Network (PON) and the propagation loss difference between the wavelengths of the TOC. The threshold adaptation conditions are also explained. Our results show that even with the worst propagation loss of existing single-mode fiber standards, it is possible to identify fiber ends 3.8 km away. However, if large bending or connection losses occur between the OTDR and the fiber end, the propagation loss difference between the wavelengths of the TOC must be improved.

Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net

The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security.

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

The type of power fiber optic cable fault event obtained by analyzing the optical time domain reflectometer (OTDR) detection curve is an important basis for ensuring the operation quality of communication lines. To address the issue of low accuracy in recognizing fault event patterns, this research proposes the AVOA-LightGBM method for optical cable fault event pattern recognition based on wavelet packet decomposition. Initially, a three-layer wavelet packet decomposition is performed on different fault events, resulting in eight characteristic signals. These signals are then normalized and used as input for each recognition model. The Light Gradient Boosting Machine (LightGBM) is optimized using the African vulture optimization algorithm (AVOA) for pattern recognition. The experimental results demonstrate that this method achieves a recognition accuracy of 98.24%. It outperforms LightGBM, support vector machine (SVM), and extreme learning machine (ELM) by 3.7%, 19.15%, and 5.67%, respectively, in terms of accuracy. Moreover, it shows a 1.8% improvement compared with the combined model PSO-LightGBM.