Fiber Vibration Research Articles

A strain transmissibility-based analysis approach for operational modal of concrete dam under nonstationary excitation

The transmissibility-based operational modal analysis (TOMA) method is attracting more attentions due to its relaxation of assumptions about excitation properties, making accurately identifying the modes of actual engineering structures possible. However, in specific application on distributed vibration responses of huge hydraulic structures excited by broad-spectrum non-stationary earthquake, more proper selections of data segmentations on two dimensions (along time or spatial axis) are needed. Based on the concept of distributed vibration sensing optical fiber strain transmissibility, the operational modal analysis model based on single reference and poly references transmissibility under the strain format and the corresponding solutions of model and modal parameters are studied. A false mode elimination method combined with the continuity of spatial distribution of optical fiber measurements, a response sequence set optimization method that comprehensively consider amplitude/PSD non-stationarity, and the principle about how to select (non) reference points (sets) considering the spatial distribution of measurement signal-to-ratio are further developed. The case study shows that the proposed combined method could improve the modal parameter identification accuracy of concrete dams under broad-spectrum non-stationary excitation, and the distributed optical fiber vibration sensing technology can provide a rich (non) reference point (set) selection combination for this method.

Low frequency vibration monitoring of wind turbine tower based on optical fiber sensor and its potential for internet of things

Among all renewable energy sources, wind energy is a cost-effective alternative energy source. The majority of wind turbines are built in harsh environments due to their power generation characteristics, which is one of the prime reasons resulting in frequent failures of wind turbine. Among various failures, the vibration of wind turbine tower cannot be ignored because it is a precursor of the failure of the wind turbine. The electrical vibration sensors have the problems of power supply and electromagnetic interference for the condition assessment of wind turbine tower. A vibration sensor based on optical Fabry-Perot (F-P) interference principle with high sensitivity is designed, fabricated and characterized to further meet the requirements of vibration detection of wind turbine tower. The mechanical simulation model of the diaphragm and optical vibration platform is constructed to verify the sensing characteristic of the F-P optical fiber vibration sensor (OFVS). The experiment results indicate a resonant frequency of the F-P OFVS of 223 Hz, an output sensitivity of 122.22 mV/m·s−2 at 10 Hz, and a horizontal output of less than 6 %. In addition, the designed F-P OFVS possesses the superiorities of compact structure, passive and excellent anti-electromagnetic interference, and has a wide application prospect in the vibration detection of the wind turbine tower.

Signal recognition based on transfer learning for Φ-OTDR fiber optic distributed disturbance sensor

Abstract Traditional classification models for optical fiber vibration signals can achieve good recognition results when there are a large number of samples. However, it is difficult to obtain sufficient training samples in real scenes. A method of Φ-OTDR sensor signal recognition based on VGGish transfer learning is proposed in this paper. Firstly, a selection indicator is designed to filter out vibration signals from sparse signals. Then, the VGGish network pretrained for sound classification is used for transfer learning as a feature extractor for optical fiber vibration signals. This method was applied to classify 6 kinds of optical fiber vibration signals. When the number of training samples is reduced to 480 samples, the method can still achieve 84.17% classification accuracy. Compared to the method of training from scratch, this proposed method reduces training time by 73.9%.

Design and characteristic analysis of micro multi-core fiber vibration sensor

We proposed and fabricated a miniaturized multi-core fiber grating vibration sensor. The size of the miniaturized vibration sensor is 10mm × 10mm × 10 mm with a mass of only 0.25g. Finite element analysis and experimental tests were carried out to validate the performance of the vibration sensor. The experiment results indicate that the sensor has a sensitivity of 68.72 pm/g in the X direction and 64.52 pm/g in the Y direction within the operating frequency range of 20–240Hz. The cross-interference between the two directions of vibration measurement falls within 4 %. The sensor is suitable for measuring mechanical vibrations in the mid-low frequency range, especially in cases where size, quality, and distributed measurement are of particular concern.

Distributed vibration and temperature sensing system by multiplexed fiber scattering spectra

A new, to the best of our knowledge, distributed optical fiber vibration and temperature hybrid sensing system is proposed and experimentally demonstrated. The proposed system only employs two signal channels, which is more compact and practical. Based on the structure of the optical time domain reflectometer (OTDR), the Rayleigh scattering light and the Raman anti-Stokes scattering light is extracted for vibration and temperature sensing, respectively. For vibration sensing, a new differential location algorithm based on polarization state analysis of the Rayleigh scattering light is proposed to locate the vibration events. It first rectifies the original OTDR traces by fiber attenuation compensation to make each position in it with the same pulse power level. And then, by difference of adjacent traces and threshold discrimination, the vibration positions are identified and located. For temperature sensing, a temperature calibration unit and algorithm are adopted to dynamically correct the trace data and reduce the temperature measurement error caused by the instability of the pulse laser source. The experiment is conducted with a fiber range of about 12 km and laser pulse width of 60 ns, and the experimental results show that the maximum error range for temperature measurement is −0.7∘C to 1.3°C, with a root mean square (RMS) error of 0.85°C in the entire temperature measurement range. Additionally, the spatial resolution (SR) for both vibration and temperature sensing is 6 m.

Fused feature extract method for Φ-OTDR event recognition based on VGGish transfer learning

Thanks to the development of artificial intelligence algorithms, the event recognition of distributed optical fiber sensing systems has achieved high classification accuracy on many deep learning models. However, the large-scale samples required for the deep learning networks are difficult to collect for the optical fiber vibration sensing systems in actual scenarios. An overfitting problem due to insufficient data in the network training process will reduce the classification accuracy. In this paper, we propose a fused feature extract method suitable for the small dataset of Φ-OTDR systems. The high-dimensional features of signals in the frequency domain are extracted by a transfer learning method based on the VGGish framework. Combined with the characteristics of 12 different acquisition points in the space, the spatial distribution characteristics of the signal can be reflected. Fused with the spatial and temporal features, the features undergo a sample feature correction algorithm and are used in a SVM classifier for event recognition. Experimental results show that the VGGish, a pre-trained convolutional network for audio classification, can extract the knowledge features of Φ-OTDR vibration signals more efficiently. The recognition accuracy of six types of intrusion events can reach 95.0% through the corrected multi-domain features when only 960 samples are used as the training set. The accuracy is 17.7% higher than that of the single channel trained on VGGish without fine-tuning. Compared to other CNNs, such as ResNet, the feature extract method proposed can improve the accuracy by at least 4.9% on the same dataset.

Image-assisted 2D partition denoising method for distributed optical fiber vibration measurements

The traditional one-dimensional denoising method, commonly applied in vibration signal processing, would encounter issues as the calculation complexity, difficulty in maintaining spatial consistency and inability to address noise distributed spatially when measurements are from multiple points. Distributed optical fiber vibration sensing technology produces measurements densely distributed on both time and spatial axes, accompanied by inevitable noise from demodulation error, uneven packaging of sensing components and environmental interference, in which condition proposing new two-dimensional denoising method becomes particularly important. Focusing on the above spatiotemporal denoising target, this paper proposes a new image-assisted 2D partition denoising method by introducing the morphological methods, improving the traditional empirical mode decomposition, and raising the partition denoising strategy. The experimental and simulated distributed vibration measurements of a cantilever beam were carried out to verify the proposed method from the perspective of modal analysis implemented by the DATA-driven stochastic subspace identification. The analysis results indicate that the proposed method could handle the spatiotemporal noise simultaneously and is in favor of improving the continuity of mode identification results especially.

Application of Distributed Acoustic Sensing Technology in Pipeline Leakage Monitoring

Pipeline leak monitoring is an important industrial safety measure designed to ensure the safety of liquids or gases during transportation. Distributed acoustic sensing (DAS) technology is based on the reverse Rayleigh scattering inside the fiber to reflect the change of the measured physical quantity, and has great advantages in monitoring range, environmental adaptability, transmission loss control and system stability. In this paper, the pipeline leakage monitoring technology based on distributed acoustic sensing fiber is used to study the leakage signal of small leak aperture. In order to improve the sensitivity of leakage monitoring, the optical fiber is spiral wound on the pipe section. The identification method of pipeline leakage signal based on fast Fourier transform is proposed. By analyzing the vibration of the optical fiber in the time domain and the frequency domain, the leakage signal can be accurately monitored. Pipeline leakage tests with different leak apertures were carried out, and the leakage locations were studied by energy attenuation and cross-correlation techniques. The experimental results show that the time-domain signal fluctuates obviously and the full-band energy of the frequency-domain signal increases after pipeline leakage. The increase of leakage diameter will gradually increase the signal energy, and the leakage energy will gradually move from high frequency to low frequency. The energy attenuation positioning technique can locate the leakage within the range of a single sensing unit, and determine the leakage location through cross-correlation analysis with an error of less than 3 m.

Frost resistance and damage evolution model of basalt fiber-reinforced recycled concrete based on recycled coarse aggregate strengthening and vibration mixing processes

A self-created grinder machine was utilized to clear the wrapped mortar from recycled coarse aggregates (RCA) in order to enhance the performance of basalt fiber-reinforced recycled aggregate concrete (BFRAC). In combination with the RCA strengthening and vibration mixing processes, the mechanical properties and freeze-thaw frost of BFRAC were tested to clarify the mechanism for improving the frost resistance under the synergistic effect of basalt fiber (BF) reinforcement and vibration mixing. Meanwhile, the impact of the production process and BF strengthening technique on RAC performance was explained from a microscopic standpoint using a scanning electron microscope (SEM) test, and aggregate-old mortar interface transition zones were significantly reduced. BF is beneficial in improving the mechanical properties and frost resistance of recycled aggregate concrete (RAC). Among them, the performance improvement effect of RAC is the most significant when using vibration mixing processes, adding BF with a length of 18 mm and a content of 0.2 %. It can increase the compressive strength of RAC by 19 % compared to ordinary mixing, the splitting tensile strength by 37.6 %, and the flexural strength by 34.4 %. A polynomial damage deterioration model was established with relative dynamic elastic modulus and compressive strength as damage variables. The results show that the model can accurately predict the degree of freeze-thaw damage of BFRAC. The research results have significant theoretical value for enhancing the frost resistance performance of RAC.

An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks

This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques.

Optical fiber vibration signal recognition based on an efficient multidimensional feature extraction network.

In the field of optical fiber vibration signal recognition, one-dimensional signals have few features. People often used the shallow layer of a one-dimensional convolutional neural network (1D-CNN), which results in fewer features being learned by the network, leading to a poor recognition rate. There are also many complex algorithms and data processing methods, which make the whole signal recognition process more complicated. Therefore, an optical vibration signal recognition method based on an efficient multidimensional feature extraction network was proposed. Based on ResNet-50, efficient channel attention (ECA) was used to improve image features extraction ability, and a long short-term memory (LSTM) network was used to enhance the extraction of temporal features. Three different vibration signals were collected using a phase-sensitive optical time-domain reflectometry (Φ-OTDR) optical fiber sensing system. Vibration signals were converted into 128×128 grayscale images, which have more effective vibration information. The experimental results show that the three types of signals can be recognized and classified effectively by the network, and the average recognition rate is 98.67%.

Localizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing

Ensuring the safe transportation of energy relies heavily on the timely safety inspection and pigging of energy pipelines using in-pipe inspection robots known as Smart/Intelligent PIGs. Addressing the potential risks of PIG block incidents and the limitations of excessive speed in the maintenance, real-time localization and tracking of these inspection robots has become imperative. However, traditional localization and tracking methods present a challenge due to their resource-intensive nature, requiring significant manpower and resources. To overcome this limitation and enhance the intelligence of the robot operation monitoring, this paper proposes an innovative artificial intelligence (AI) integration algorithm framework based on distributed optical fiber sensing (DOFS) for real-time localization and tracking of robots. It adopts noise reduction and reconstruction techniques in signal processing, effectively enhancing the quality of optic fiber vibration signals. Notably, the integration of two distinct features that complement each other enables dual verification of tracking detection, ultimately bolstering the system’s effectiveness and trustworthiness. Besides, a logical reasoning-based localization decision strategy further enhances the system’s capabilities, allowing for controlled tracking intervals and step sizes that can be tailored to meet the task requirements under diverse working conditions. The collaboration of three modules makes it feasible to monitor dynamic changes of the detection robot in the pipeline along the direction of operation. The experimental results convincingly demonstrate that the integrated framework possesses several key advantages of remarkable robustness, real-time performance, and minimal error. It underscores the system’s potential to ensure energy pipeline safety efficiently and effectively.

Recognition of Fiber Optic Vibration Signals Based on Laplace Wavelet Transform and Deep Learning

AbstractConvolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%.

Perimeter monitoring of urban buried pipeline threated by construction activities based on distributed fiber optic sensing and real-time object detection.

Urban construction activities seriously jeopardize the security of buried pipeline. Distributed optical fiber vibration monitoring is one of the most promising ways to prevent third-party threats, of which the biggest challenge is to quickly and accurately detect rare abnormal events from extremely large amounts of time-space raw data. By analogy with image recognition, the task here is similar to object detection if considering the time-space optical signals as the grayscale images and the abnormal events as the objects. Given this, what we believe to be a novel monitoring method is proposed, which consists of two Faster R-CNN models, a max pooling layer and a monitoring strategy. In the field tests, the 86-hour optical vibration signals for 5.25 km distance are recognized within 6.6 minutes with the recognition rate of 98.85% for construction activities, and only two false alarms are issued. The proposed method can reduce the recognition time by 99.59% compared to the CNN-based method.

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique

The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach–Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.

On Bayesian Optimization-Based CNN-BiLSTM Network for Multiclass Classification in Distributed Optical Fiber Vibration Sensing Systems

On Bayesian Optimization-Based CNN-BiLSTM Network for Multiclass Classification in Distributed Optical Fiber Vibration Sensing Systems

Distributed Optical Fiber Vibration Signal Recognition Technology Based on Gramian Angular Field

Distributed Optical Fiber Vibration Signal Recognition Technology Based on Gramian Angular Field

Multi-Dimensional Distributed Optical Fiber Vibration Sensing Pattern Recognition Based on Convolutional Neural Network

Multi-Dimensional Distributed Optical Fiber Vibration Sensing Pattern Recognition Based on Convolutional Neural Network

Precise disturbance localization of long distance fiber interferometer vibration senor based on an improved time–frequency variation feature extraction scheme

Locating intrusions with high accuracy is crucial for prompting long distance distributed optical fiber vibration sensing systems to practical applications in engineering filed. To achieve the high measurement accuracy and real-time performance simultaneously, in this paper, an intensity-modulated ultra-long distance distributed optical fiber vibration sensing system using an asymmetric dual Mach-Zehnder interferometer is experimentally established. More importantly, a positioning approach based on an ameliorated time–frequency feature extraction scheme is proposed with both theoretically analysis and experimental demonstration. In particular, an endpoint detection algorithm based on differential operation is firstly utilized to rapidly and accurately locate the endpoints of the received vibration signals. Thus the large-band width signals can be successfully acquired by setting an approximate window. Then, the ameliorated time–frequency feature extraction scheme based on a maximal overlap discrete wavelet packet transform is applied to eliminate the asymmetry of the large-band width signals, as it could lead to an erroneous positioning result when directly using a cross-correlation time delay estimation algorithm. Finally, the vibration positions along the sensing fiber link of the established sensing system can be successfully demodulated with the help of the extracted time–frequency features. To validate the effectiveness of the proposed scheme, vibration positioning tests were conducted in a real perimeter security scenario. And the results show that a mean positioning error of 11.12 m and a mean processing time of 0.276 s have been realized in a sensing length of 101 km. Thus, it is believed that through continuous technological advancements and refine algorithm optimization, the long distance distributed optical fiber vibration sensor founded on the proposed positioning strategy is poised to play an important role in the practical engineering applications.