Search Results (164)

Vibration measurements are essential for the early detection of faults in rotating machinery and are particularly important for hydrogenerators in hydro power plants. Industrial applications of vibration measurements typically rely on displacement, velocity, and acceleration sensors, each offering distinct advantages and limitations. This paper discusses and proposes a cost-effective, contactless optical vibration sensing system based on standard fiber-optic telecommunication components, enabling its integration into existing fiber-optic networks. The proposed system utilizes interferometric sensing principles, providing inherent immunity to electromagnetic interference and galvanic effects while achieving micrometer-scale resolution. The key advancement of the proposed sensor lies in the relatively simple and cost-effective configuration—the realization of the Michelson interferometer. It facilitates a combination of standard optical communication hardware, including a 3 × 3 fiber-optic coupler and two photodetectors for reliable discrimination of vibration displacement directions. The associated signal processing platform is based on a cross-correlation algorithm by which both the direction and the magnitude of displacement are determined. The optical sensor was experimentally validated using a realistic-scenario laboratory setup, which demonstrates the feasibility and performance of the proposed approach. Full article

To address the demand for early hydrogen monitoring in power equipment insulation systems, a fiber-optic Fabry–Perot (F-P) hydrogen sensor based on a Pd–Cu alloy microcantilever is proposed. The microcantilever serves as the force-sensitive structure, with a Pd–Cu alloy film deposited as the hydrogen-sensitive layer and an Au reflective layer introduced to enhance optical reflection and suppress thermal drift. Hydrogen absorption induces volume expansion of the Pd–Cu film, causing cantilever bending and a consequent variation in the F-P cavity length, which leads to a shift in the characteristic wavelength of the reflected spectrum and enables wavelength-demodulated hydrogen detection. Finite element analysis was conducted to investigate the stress distribution and displacement response, confirming the effective amplification effect of the microcantilever structure. Sensor fabrication, packaging, and hydrogen response experiments were subsequently carried out. The results show a good linear response in the hydrogen concentration range of 0–300 ppm, with a wavelength sensitivity of approximately 20.8 pm/ppm and a limit of detection of 3.24 ppm. In a 24 h stability test, the baseline fluctuation standard deviation was 22.46 pm, indicating good stability and repeatable sensing performance. Temperature variation produced a wavelength sensitivity of approximately 0.2734 nm/°C, and environmental condition tests further demonstrated stable operation under temperature, humidity, vibration, and electromagnetic disturbances. The proposed sensor exhibits immunity to electromagnetic interference, intrinsic safety, and compatibility with miniaturized integration, showing promising potential for low-concentration hydrogen monitoring in power equipment. Full article

This paper proposes a high-sensitivity and sensitivity-tunable vibration sensing system based on a switchable loop length optoelectronic oscillator (OEO). Carrier-sideband separation is realized by using an acousto-optic modulator (AOM), and the resonant cavity length is designed to be independent of the sensing fiber arm. Compared with a conventional 10 GHz OEO under the same total loop delay condition, the proposed architecture provides a theoretical sensitivity enhancement of approximately $1.93\times {10}^4$, without requiring a high RF oscillation frequency. Meanwhile, the system oscillates at only 80 MHz, which greatly reduces the implementation difficulty of the frequency detection circuit. The proposed scheme further introduces a mechanical optical switch (MOS) to select intra-loop fibers of different lengths, thereby reconfiguring the equivalent loop delay and the free spectral range of the OEO. Experimental results show that stable single-mode oscillation is achieved at 80.42 MHz with a side-mode suppression ratio of 51 dB. By selecting loop fiber lengths of 1200 m, 500 m and 0 m, frequency-to-displacement sensitivities of 0.892 GHz/cm, 1.93 GHz/cm and 9.27 GHz/cm are obtained respectively, with excellent linearity. A 600 Hz vibration signal is successfully demodulated with a signal-to-noise ratio of 72.1 dB. The proposed method provides a simple and reconfigurable solution for high-precision vibration measurement under different operating conditions. Full article

This study demonstrates single-channel fiber Bragg grating (FBG) sensing for relative vibration-state monitoring of a motor–support system under angle-dependent boundary conditions. A packaged FBG accelerometer-type sensing unit was mounted on the motor–support structure, and the reflected Bragg wavelength was recorded as a one-dimensional optical vibration response. Because the sensor was installed away from the rotating shaft, the measured wavelength fluctuation was interpreted as a coupled vibration-sensitive response of the motor, fixture, sensor package, bonding condition, and changing boundary state, rather than as a calibrated shaft speed or absolute acceleration signal. Adaptive variational mode decomposition (AVMD) was applied to track the time-varying narrowband spectral-response trajectory of the Bragg-wavelength signal. In parallel, raw wavelength windows were supplied to LSTM, 1D-CNN, and CNN–LSTM autoencoders to evaluate waveform departures from learned nominal fixed-angle behavior. The fixed-angle results showed stable but distinguishable optical vibration responses under different boundary states, whereas the dynamic angle-transition records produced local trajectory changes and alarm-candidate intervals. Baseline and autoencoder comparisons further clarified the trade-off between transition coverage and false-alarm tendency. The RMS threshold baseline was more sensitive to transition-related amplitude changes but produced more false alarms, whereas the CNN–LSTM autoencoder provided the most selective response among the tested autoencoder branches. The results are interpreted as task-specific evidence for relative vibration-state transition monitoring rather than as general motor fault diagnosis. Overall, the framework demonstrates a compact FBG-based route for relative vibration-state transition monitoring when speed references, dense sensor layouts, and labeled fault data are unavailable. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article

Today, distributed optical fiber sensors are widely used in structural health monitoring due to their high sensitivity and long-distance applicability. However, when embedded in pavement structures, distributed optical fiber sensors are always installed in a slotted buried fashion, which not only affects current pavement durability but also reduces pavement construction efficiency. In order to design clear requirements of in situ-embedded distributed optical fiber sensors for pavement construction, this study analyzes the micro-mechanical behavior of optical cables under the ultimate pavement compaction state based on a coupled DEM-FDM approach. According to the study results, it is found that when the pavement subbase was compacted, the maximum contact force of 13.2 mm aggregates in the Z-direction exceeds 150 N, which is the main resistance of the external load during pavement construction. The tight-buffered optical cable without reinforcement element and armored layer cannot withstand the vibration load. The inclusion of GFRP strengthening components and an armored layer decreased maximum stress by 38.2% (X), 30.6% (Y), and 30.9% (Z), as well as displacement by 64.6% (X), 45.5% (Y), and 66.7% (Z). Additionally, the thickness of the outer sheath enhanced the ability to withstand tension but not compression. The increase in the thickness of the armored layer can improve the ability to withstand tension and compression. Full article

In response to the key issues of complex internal structure, significant attenuation of partial discharge (PD) ultrasound signal propagation, and low sensor sensitivity in large oil–immersed power transformers, this paper analyzes the multi–order resonant mode vibration characteristics of the shaft–type fiber optic ultrasound sensor core structure. The displacement distribution patterns of the core structure in both transverse and longitudinal resonant modes are clarified. A strategy using oblique fiber winding rings is proposed to eliminate the problems of strain cancellation and non–accumulation of displacement in transverse and longitudinal resonant modes, which are common in traditional fiber optic ultrasound sensors with parallel fiber windings. Furthermore, design principles are provided to enhance the coverage of the free end and the high–strain regions with semi–high symmetry, as well as the vector–integrated response suitable for multi–order modes. Experimental results show that, in typical PD model detection, the oblique winding sensor exhibits a more prominent response near the high–order resonances of the core, with a detection sensitivity approximately 2.5 times higher than that of the parallel winding structure, and an overall sensitivity at least 7.4 times greater than that of traditional Piezoelectric (PZT) sensors. This demonstrates that the fiber winding method is a key design parameter determining the acoustic–solid coupling efficiency and high sensitivity performance of shaft–type fiber optic interferometric PD sensors, providing a feasible path for high–reliability fiber optic sensing solutions for online monitoring of transformer partial discharges. Full article

This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms. Full article

This study presents the experimental optimization of an interferometric optical fiber sensor for acoustic emission (AE) detection. The system employs a simple and low-cost structure composed of sensing and reference fibers, enabling interference-based detection without specialized components such as fiber Bragg gratings or Fabry–Perot cavities. A narrowband laser source was selected through comparative experiments for its superior stability and interference performance. The influence of fiber-loop parameters, including the number of turns and the optical-path intensity ratio, was systematically evaluated to clarify their effects on AE sensitivity and frequency response. The experimental results demonstrate that detection performance and bandwidth can be flexibly tuned by optimizing the loop configuration. Finally, the sensor was validated using a tensile test, successfully detecting AE signals in the range of 20 kHz to 1 MHz. The proposed system provides a robust, EMI-resistant, and cost-effective interferometric solution for AE monitoring. Full article

Distributed fiber-optic sensing (DFOS) with intentionally spaced mechanical fixity points was experimentally evaluated for the structural health monitoring (SHM) of reinforced concrete (RC) members. A full-scale four-point bending test was conducted on a 12 m RC beam (400 × 400 mm) instrumented with a single-mode DFOS cable incorporating internal anchors at 2 m intervals and bonded externally with structural epoxy. Brillouin time-domain analysis (BOTDA) provided distributed strain measurements at approximately 0.5 m spatial resolution, with all cables calibrated to ±15,000 µε. Under stepwise monotonic loading, the system captured smooth, repeatable strain baselines and clearly resolved localized tensile peaks associated with crack initiation and propagation. Long-gauge averages exhibited a near-linear load–strain response (R² ≈ 0.99) consistent with discrete foil and vibrating-wire strain gauges. Even after cracking, the DFOS signal remained continuous, while some discrete sensors showed saturation or scatter. Temperature compensation via a parallel fiber ensured thermally stable interpretation during load holds. The fixed-point configuration mitigated local debonding effects and yielded unbiased long-gauge strain data suitable for assessing serviceability and differential settlement. Overall, the results confirm the suitability of fixed-point DFOS as a durable, SHM-ready sensing approach for RC foundation elements and as a dense data source for emerging digital-twin frameworks. Full article

A mechanical structure of a fiber Fabry–Perot interferometric vibration sensor for monitoring oil and gas pipelines has been proposed, and design analysis research on performance improvement has been carried out. By designing a serpentine beam structure, the mechanical sensitivity of the sensor is enhanced. Meanwhile, by designing a vertically symmetrical gravity-sensing structure, the cross-axis sensitivity of the sensor is reduced. The results of simulation analysis show that the mechanical sensitivity of the proposed design structure is 89.20 μm/g, which is 32.44 times that of the conventional structure. Moreover, due to the design of low cross-axis sensitivity, the optical sensitivity of the vibration sensor will not be degraded because of its installation status on the pipeline. The proposed mechanical structure provides a design reference for the application of the fiber Fabry–Perot interferometric vibration sensor on oil and gas pipelines, and offers potential for the development of a high-performance comprehensive safety risk monitoring system for oil and gas pipelines. Full article

Structural Health Monitoring (SHM) of steel bridges is vital for ensuring the longevity, safety, and reliability of critical transportation infrastructure. This review synthesizes recent advancements in SHM technologies and methodologies for steel bridges, highlighting the shift from traditional vibration-based monitoring to data-driven, intelligent systems. It covers core technological themes, including various sensing systems such as wireless sensor networks, fiber optics, and piezoelectric transducers, along with the impact of machine learning, artificial intelligence, and statistical pattern recognition. The paper explores applications for damage detection, such as fatigue life assessment and monitoring of components like expansion joints. Persistent challenges, including deployment costs, data management complexities, and the need for real-world validation, are addressed. The future of SHM lies in integrating diverse sensing technologies with computational analytics, advancing from periodic inspections to continuous, predictive infrastructure management, which enhances bridge safety, resilience, and economic sustainability. Full article

Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by analyzing complex spatio-temporal data patterns. However, the high dimensionality and noise content of raw DAS data presents significant challenges for effective feature extraction and event classification, particularly when computational efficiency is required for real-time deployment. Traditional approaches or current machine learning methods often struggle with the balance between information preservation and computational complexity. This study addresses the critical need for efficient and accurate feature extraction methods that can identify informative signal components while maintaining real-time processing capabilities in DAS-based security systems. Here we show that wavelet packet decomposition (WPD) combined with a cascaded machine learning approach achieves 98% classification accuracy while reducing computational load through intelligent channel selection and preliminary filtering. Our modified peak signal-to-noise ratio metric successfully identifies the most informative frequency bands, which we validate through comprehensive neural network experiments across all possible WPD channels. The integration of principal component analysis with logistic regression as a preprocessing filter eliminates a substantial portion of non-target events while maintaining high recall level, significantly improving upon methods that processed all available data. These findings establish WPD as a powerful preprocessing technique for distributed sensing applications, with immediate applications in critical infrastructure protection. The demonstrated gains in computational efficiency and accuracy improvements suggest broad applicability to other pattern recognition challenges in large-scale sensor networks, seismic monitoring, and structural health monitoring systems, where real-time processing of high-dimensional acoustic data is essential. Full article

Geohazards pose significant dangers to human safety, infrastructures, and the environment, highlighting the need for advanced monitoring techniques for early damage detection and structure management. The distributed optical fiber sensors (DFOS) are strain, temperature, and vibration monitoring tools characterized by minimal intrusiveness, accuracy, ease of deployment, and the ability to perform measurements with high spatial resolution. Although these sensors rely on well-established measurement techniques, available for over 40 years, their diffusion within monitoring and early warning systems is still limited, and there is a certain mistrust towards them. In this regard, based on several case studies, the implementation of DFOS for early warning of various geotechnical hazards, such as landslides, earthquakes and subsidence, is discussed, providing a comparative analysis of the typical advantages and limitations of the different systems. The results show that real-time monitoring systems based on well-established distributed fiber-optic sensing techniques are now mature enough to enable reliable and long-term geotechnical applications, identifying a market segment that is only minimally saturated by using other monitoring techniques. More challenging remains the application of the technique for vibration detection that still requires improved interrogation technologies and standardized practices before it can be used in large-scale, real-time early warning systems. Full article