Search Results (106)

This study introduces a novel deep neural network (DNN) framework tailored to breaking the sampling limit for high-frequency vibration recognition using fiber Bragg grating (FBG) sensors in conjunction with low-power, low-sampling-rate FBG interrogators. These interrogators, while energy-efficient, are inherently limited by constrained acquisition rates, leading to severe undersampling and the obfuscation of fine spectral details essential for accurate vibration analysis. The proposed method circumvents this limitation by operating solely on raw time-domain signals, learning to recognize high-frequency and extremely close vibrational components accurately. Extensive validation using the combination of simulated and experimental datasets demonstrates the model’s superiority in frequency discrimination across a broad vibrational spectrum. This approach is expected to be a significant advancement in intelligent optical vibration sensing and compact, low-power condition monitoring solutions in complex environments. Full article

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article

The growing reliance on natural gas in urban China has heightened the urgency of maintaining pipeline integrity, particularly in environments prone to disruption by nearby construction activities. In this study, we present a practical approach for the real-time classification of distributed fiber optic monitoring signals, leveraging a hybrid framework that combines the feature learning capacity of a one-dimensional convolutional neural network (1D-CNN) with the classification robustness of a support vector machine (SVM). The proposed method effectively distinguishes various pipeline-related events—such as minor leakage, theft attempts, and human movement—by automatically extracting their vibration patterns. Notably, it addresses the common shortcomings of softmax-based classifiers in small-sample scenarios. When tested on a real-world dataset collected via the DAS3000 system from Hangzhou Optosensing Co., Ltd., the model achieved a high classification accuracy of 99.92% across six event types, with an average inference latency of just 0.819 milliseconds per signal. These results demonstrate its strong potential for rapid anomaly detection in pipeline systems. Beyond technical performance, the method offers three practical benefits: it integrates well with current monitoring infrastructures, significantly reduces manual inspection workloads, and provides early warnings for potential pipeline threats. Overall, this work lays the groundwork for a scalable, machine learning-enhanced solution aimed at ensuring the operational safety of critical energy assets. Full article

In this paper, a MgO-based high-temperature Fabry-Perot (F-P) vibration sensor with a fiber-optic collimator is proposed and experimentally demonstrated at 1000 °C. The sensor is composed of a sensing unit and a fiber-optic collimator. The F-P cavity is formed by the upper surface of the inertial mass block and the countersunk hole of the cover layer. The length of the F-P cavity changes with external vibrations. The sensing unit is prepared by wet etching technology and three-layer direct bonding technology, which ensure its stability and reliability in high-temperature environments. The experimental results indicate that the sensor can operate stably within a range from room temperature up to 1000 °C. The sensitivity and non-linearity of the sensor at 1000 °C are 1.3224 nm/g and 3.8%, respectively. Furthermore, the sensor operates at frequencies of up to 4 kHz while remaining unaffected by lateral vibration signals. The high-temperature F-P vibration sensor can effectively deal with the fiber damage in extreme environments and exhibits considerable potential for widespread applications. Full article

In order to solve the problem that the vibration signal of transformer is affected by noise and electromagnetic interference, resulting in low accuracy of fault diagnosis mode recognition, a CEEMDAN-MRAL fault diagnosis method based on Fiber Bragg Grating (FBG) was proposed to quickly and accurately evaluate the vibration fault state of transformer.The FBG sends the wavelength change in the optical signal center caused by the vibration of the transformer to the demodulation system, which obtains the vibration signal and effectively avoids the noise influence caused by strong electromagnetic interference inside the transformer. The vibration signal is decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the wavelet threshold denoising algorithm improves the signal-to-noise ratio (SNR) to 1.6 times. The Markov transition field (MTF) is used to construct a training and test set. The unique MRAL-Net is proposed to extract the spatial features of the signal and analyze the time series dependence of the features to improve the richness of the signal feature scale. This proposed method effectively removes the noise interference. The average accuracy of fault diagnosis of the transformer winding core reaches 97.9375%, and the time taken on the large-scale complex training set is only 1705 s, which has higher diagnostic accuracy and shorter training time than other models. Full article

Accurately identifying optical fiber vibration signals is crucial for ensuring the proper operation of optical fiber perimeter security warning systems. To enhance the recognition accuracy of intrusion events detected by the distributed acoustic sensing system (DAS) based on phase-sensitive optical time-domain reflectometer (φ-OTDR) technology, we propose an identification method that combines empirical mode decomposition (EMD) with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. First, the EMD algorithm decomposes the collected original optical fiber vibration signal into several intrinsic mode functions (IMFs), and the correlation coefficient between each IMF and the original signal is calculated. The signal is then reconstructed by selecting effective IMF components based on a suitable threshold. This reconstructed signal serves as the input for the network. CNN is used to extract time-series features from the vibration signal and LSTM is employed to classify the reconstructed signal. Experimental results demonstrate that this method effectively identifies three different types of vibration signals collected from a real-world environment, achieving a recognition accuracy of 97.3% for intrusion signals. This method successfully addresses the challenge of φ-OTDR pattern recognition and provides valuable insights for the development of practical engineering products. Full article

Achieving both accurate and real-time monitoring heartbeat signals by non-invasive sensing techniques is challenging due to various noise interferences. In this paper, we propose an enhanced discrete wavelet transform (DWT) method that incorporates objective denoising quality assessment metrics to determine accurate thresholds and adaptive threshold functions. Our approach begins by denoising ECG signals from various databases, introducing several types of typical noise, including additive white Gaussian (AWG) noise, baseline wandering noise, electrode motion noise, and muscle artifacts. The results show that for Gaussian white noise denoising, the enhanced DWT can achieve 1–5 dB SNR improvement compared to the traditional DWT method, while for real noise denoising, our proposed method improves the SNR tens or even hundreds of times that of the state-of-the-art denoising techniques. Furthermore, we validate the effectiveness of the enhanced DWT method by visualizing and comparing the denoising results of heartbeat signals monitored by fiber-optic micro-vibration sensors against those obtained using other denoising methods. The improved DWT enhances the quality of heartbeat signals from non-invasive sensors, thereby increasing the accuracy of cardiovascular disease diagnosis. Full article

Distributed Acoustic Sensing (DAS) has emerged as a groundbreaking technology in seismology, transforming fiber-optic cables into dense, cost-effective seismic monitoring arrays. DAS makes use of Rayleigh backscattering to detect and measure dynamic strain and vibrations over extended distances. It can operate using both pre-existing telecommunication networks and specially designed fibers. This review explores the principles of DAS, including Coherent Optical Time Domain Reflectometry (COTDR) and Phase-Sensitive OTDR (ϕ-OTDR), and discusses the role of optoelectronic interrogators in data acquisition. It examines recent advancements in fiber design, such as helically wound and engineered fibers, which improve DAS sensitivity, spatial resolution, and the signal-to-noise ratio (SNR). Additionally, innovations in deployment techniques include cemented borehole cables, flexible liners, and weighted surface coupling to further enhance mechanical coupling and data accuracy. This review also demonstrated the applications of DAS across earthquake detection, microseismic monitoring, reservoir characterization and monitoring, carbon storage sites, geothermal reservoirs, marine environments, and urban infrastructure surveillance. The study highlighted several challenges of DAS, including directional sensitivity limitations, vast data volumes, and calibration inconsistencies. It also addressed solutions to these problems, such as advances in signal processing, noise suppression techniques, and machine learning integration, which have improved real-time analysis and data interpretability, enabling DAS to compete with traditional seismic networks. Additionally, modeling approaches such as full waveform inversion and forward simulations provide valuable insights into subsurface dynamics and fracture monitoring. This review highlights DAS’s potential to revolutionize seismic monitoring through its scalability, cost-efficiency, and adaptability to diverse applications while identifying future research directions to address its limitations and expand its capabilities. Full article

The observation and evaluation of vibration signals is crucial for enhancing engineering quality and ensuring the safe operation of equipment. This paper proposes a fiber-optic vibration sensor based on the Sagnac interference principle. The polarization-maintaining fiber (PMF) is spliced between two single mode fibers (SMFs) to form the SMF-PMF-SMF (SPS) fiber structure. The Sagnac interferometer consists of an SPS fiber structure connected to a 3 dB coupler. Due to the principle of the elastic-optical effect, the interferometric spectrum of the PMF-based Sagnac interferometric structure changes when the PMF is subjected to stress, enabling vibration to be measured. The experimental results show that the relative measurement error of the fiber-optic vibration sensor for healthy and faulty bearings is less than 1.8%, which verifies the effectiveness and accuracy of the sensor. The sensor offers benefits of excellent anti-vibration fatigue characteristics, simple production, small size, light weight, and has a wide range of applications in mechanical engineering, fault detection, safety and security, and other fields. Full article

The development of optical methods for surface vibration measurements is currently of great interest. We propose a modified tandem low-coherence technique that utilizes the phase information of the low-coherence signal to detect surface vibrations. The resolution of this scheme is less than 1 nm for a 20 kHz bandwidth. The proposed technique is not just limited to measurements of surface vibrations, but it can also be used for interferometric fiber-optic sensors. Full article

A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption. However, existing recognition algorithms generally suffer from problems such as the inability to recognize long-distance moving targets and adapt to new environments, as well as low recognition accuracy. Here, we demonstrate that applying transfer learning to recognition algorithms can adapt to new environments and improve accuracy. We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. We used transfer learning to make the algorithm more adaptable to environmental changes. Our results show that the proposed moving ground-target recognition algorithm can identify target types, improve accuracy, and adapt to a new environment with good performance. We anticipate that our algorithm will be the starting point for more complex recognition algorithms. For example, target recognition algorithms based on multi-modal fusion and transfer learning can better meet actual needs. Full article

Laser Doppler vibrometry (LDV) is an essential tool in assessing by evaluating ossicle vibrations. It is used in fundamental research to understand hearing physiology better and develop new surgical techniques and implants. It is also helpful for the intraoperative hearing assessment and evaluation of postoperative treatment results. Traditional volumetric LDVs require access in a straight line to the test object, which is challenging due to the structure of the middle ear and the way the auditory ossicles are accessible. Here, we demonstrate the usage of a fiber-based laser Doppler vibrometer (FLDV) for middle ear diagnostics. Compared to classical vibrometers, the main advantages of this device are the ability to analyze several arbitrarily selected points simultaneously and the flexibility achieved by employing fiber optics to perform analysis in hard-to-reach locations, which are particularly important during endoscopic ear surgery. The device also allows for a simple change in measuring probes depending on the application. In this work, we demonstrate the properties of the designed probe and show that using it together with the FLDV enables recording vibrations of the auditory ossicles of the human ear. The obtained signals enable hearing analysis. Full article

An all-fiber vibration sensor based on the Fabry-Perot interferometer (FPI) is proposed and experimentally evaluated in this study. The sensor is fabricated by introducing a Fabry-Perot cavity to the single-mode fiber using femtosecond laser ablation. The cavity and the tail act together as a cantilever beam, which can be used as a vibration receiver. When mechanical vibrations are applied, the cavity length of the Fabry-Perot interferometer changes accordingly, altering the interference fringes. Due to the low moment of inertia of the fiber optic cantilever beam, the sensor can achieve broadband frequency responses and high vibration sensitivity without an external vibration receiver structure. The frequency range of sensor detection is 70 Hz–110 kHz, and the sensitivity of the sensor is 60 mV/V. The sensor’s signal-to-noise ratio (SNR) can reach 56 dB. The influence of the sensor parameters (cavity depth and fiber tail length) on the sensing performance are also investigated in this study. The sensor has the advantages of compact structure, high sensitivity, and wideband frequency response, which could be a promising candidate for vibration sensing. Full article

A new-type vibration sensor based on a fiber Bragg grating combined with a special structure-packaged design is proposed for monitoring the mechanical vibration signals. Three different sensing structures, including the film squeeze type, new film squeeze type, and elastic tape squeeze type are proposed for measuring the vibration signals with the frequency range from tens to thousands of Hz. In the comparison to experimental results, the new film squeeze structure has a nice sensing performance in the range from 100 to 1000 Hz with a sensitivity of 0.302 mV/g. For the elastic tape squeeze structure, the elastic tape is designed to encapsulate the optical fiber with a good frequency response from 1100 to 3100 Hz. In addition, by using the new film squeeze structure to measure the steady-state and non-steady-state vibration signals, the spectral components of sensing signals are analyzed by using the wavelet transformation for confirming the testing signals. These vibration fiber sensors can be applied in the measurement of high-end manufacture-facility vibration or earthquake vibrations etc. Full article

This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others. The optical output signal consists of the overlap between the FBG reflection and the reflection at the fiber-cleaved tip. This new fiber optic device has been studied as a vibration sensor, allowing for the characterization of this sensor in the frequency range of 150 Hz to 70 kHz. How the phase shift in the FBG can be controlled by changing the length of the last pitch is also shown. This device can be used as a filter and a sensing element. As a sensing element, we will demonstrate its application as a vibration sensor that can be utilized in various applications, particularly in monitoring mechanical structures. Full article