Fiber Optic Sensor System Research Articles

Experimental Verification of GFRP Bridge Deck Panels Using an Integrated Distributed Fiber Optic Sensing System

Experimental Verification of GFRP Bridge Deck Panels Using an Integrated Distributed Fiber Optic Sensing System

A Study on Sensitivity Improvement of the Fiber Optic Acoustic Sensing System Based on Sagnac Interference.

A new pickup structure was introduced and modified to improve the resolution of the linear Sagnac optical fiber acoustic sensing system. The maximum strains corresponding to the material, diameter, wall thickness, and height of the pickups were analyzed by simulation. An aluminum cylinder with a diameter of 110 mm, a wall thickness of 3 mm, and a height of 120 mm was chosen as the basic pickup. A four-groove pickup with a vertical width of 80 mm and a horizontal width of 20 mm was introduced to improve the sensitivity of the system. The experiments showed that the average peak-to-peak sensitivity of the four-groove pickup increased by 215.54% to 106.806 mV/Pa. The improved pickup can be applied in areas to monitor the situation of invasion of the Sagnac optical fiber acoustic sensing system.

New method for the investigation of mode coupling in graded-index polymer photonic crystal fibers using the Langevin stochastic differential equation

The mode coupling in a graded-index polymer photonic crystal fiber (GI PPCF) with a solid core has been investigated using the Langevin equation. Based on the computer-simulated Langevin force, the Langevin equation is numerically integrated. The numerical solutions of the Langevin equation align with those of the time-independent power flow equation (TI PFE). We showed that by solving the Langevin equation, which is a stochastic differential equation, one can successfully treat a mode coupling in GI PPCFs, which is an intrinsically stochastic process. We demonstrated that, in terms of effectiveness, the Langevin equation is preferable compared to the TI PFE. The GI PPCF achieves the equilibrium mode distribution (EMD) at a coupling length that is even shorter than the conventional GI plastic optical fiber (POF). The application of multimode GI PCFs in communications and optical fiber sensor systems will benefit from these findings.

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

Machine learning approach in multi-channel fiber-optic SPR sensors

Fiber-optic surface plasmon resonance (SPR) sensors have been increasingly used due to their advantages such as compact size, stable physical and chemical properties, high sensitivity, and resistant to electromagnetic interference. In this paper, a dual-channel side-polished fiber-optic SPR sensor system is established to detect the refractive index of liquids and the liquid temperature. High sensitivity of ∼2000 nm/RIU (refractive index unit) and linear sensitivity of ∼−2.0 nm/°C is achieved for two-channel SPR sensors, which are integrated for simultaneously RI temperature sensing and easily scaled to multi-channel SPR sensor array. The resonant wavelengths of the two sensors are located in the range of 600–700 nm and 700–800 nm respectively, which could avoid overlapping the resonance dips. Then, we propose an approach using five machine learning algorithms to predict the resonant wavelength of the second channel of SPR temperature sensor using the normalized transmission spectrum data with a wavelength range of 760–889 nm. After error analysis, it is found that the Categorical Boosting (CatBoost) is the best among five algorithms in terms of accuracy and resolution. The results provide clear evidence that accurate SPR resonant wavelength can be obtained with machine learning approach using a more compact spectrometer.

An Image Processing-Based Correlation Method for Improving the Characteristics of Brillouin Frequency Shift Extraction in Distributed Fiber Optic Sensors

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (Brillouin optical time domain analysis/reflectometry) principles. First, the spectra corresponding to different spatial coordinates of the fiber sensor are resampled. Subsequently, the resampled spectra are aligned by the position of the maximum by shifting in frequency relative to each other. The spectra aligned by the position of the maximum are then averaged, which effectively increases the signal-to-noise ratio (SNR). Finally, the Lorentzian curve fitting (LCF) method is applied to the spectrum with improved characteristics, including a reduced scanning step and an increased SNR. Simulations and experiments have demonstrated that the method is particularly efficacious when the signal-to-noise ratio does not exceed 8 dB and the frequency scanning step is coarser than 4 MHz. This is particularly relevant when designing high-speed sensors, as well as when using non-standard laser sources, such as a self-scanning frequency laser, for distributed fiber-optic sensing.

Distributed polymer optical fiber sensors using digital I-OFDR for geotechnical infrastructure health monitoring

We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geotextiles which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geotextile-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements. In order to reduce overall costs of the sensor system and achieve its robustness required for use on site, the standard vector network analyzer used by default was replaced by an integrated digital board for optical frequency domain analysis. This implementation showed a significant improvement in the dynamic range and sensitivity compared to the previous measurement system based on the conventional network analyzer. The functionality of the entire sensor system was proven in the field test during the installation of two geomats with integrated PF-POFs embedded into a road embankment in a section of the B 91 federal road near Werschen in the greater Leipzig area. To monitor the installation-related changes in the condition of both sensor-based geomats several control measurements on the embedded PF-POFs and co-integrated reference glass fibers were carried as the construction works progressed. The measurements recorded in the field confirm the great potential of the selected PF-POF used as the distributed fiber optic strain sensor. The future management of the remaining challenges of industrial strain-coupled sensor integration can result in a successful market launch of the PF-POF-sensor integrated into geosynthetics. The sensor system is ready to be used for practical application.

Comprehensive hull structures diagnostics using a distributed fiber-optic sensors system

The possibility of constructing real-time systems that provide vibration diagnostics and acoustic diagnostics of complex man-made objects hull structures based on distributed fiber-optic sensors is substantiated. The problem of detecting and localizing sources of acoustic emission using a fiber-optic measuring system placed on a surface housing structure is considered. When solving the detection problem, the flow of acoustic emission signals is characterized by a statistically average pulse repetition rate, their amplitude and time distribution. All parameters of emission signals are random variables with a priori unknown characteristics. To solve the detection problem, the sequential Wald analysis algorithm is used according to the “M out of N” detection criterion. A fiber-optic measuring system has been developed and manufactured, combining the capabilities of vibration diagnostics and acoustic diagnostics of the hull structure. To increase the noise immunity of a fiber-optic measuring system, it is proposed to interrogate it with a broadband signal, for example, a signal with linear frequency modulation.

Event Pattern Recognition of Distributed Optical Fiber Sensing System Based on FES-RDB-CNN and Voting Classifier Combination

Event Pattern Recognition of Distributed Optical Fiber Sensing System Based on FES-RDB-CNN and Voting Classifier Combination

NP-Doped Fiber Smart Tendon: A Millimeter-Scale 3-D Shape Reconstruction With Embedded Distributed Optical Fiber Sensor System

NP-Doped Fiber Smart Tendon: A Millimeter-Scale 3-D Shape Reconstruction With Embedded Distributed Optical Fiber Sensor System

Intrarenal pressure detection during flexible ureteroscopy with fiber optic pressure sensor system in porcine model

To validate the feasibility of a fiber-optic pressure sensor-based pressure measurement device for monitoring intrarenal pressure and to analyze the effects of ureteral acess sheath (UAS) type, surgical location, perfusion flow rate, and measurement location on intrarenal pressure (IRP). The measurement deviations and response times to transient pressure changes were compared between a fiber-optic pressure sensing device and a urodynamic device IRP in an in vitro porcine kidney and in a water tank. Finally, pressure measurements were performed in anesthetized female pigs using fiber-optic pressure sensing device with different UAS, different perfusion flow rates, and different surgical positions at different renal calyces and ureteropelvic junctions (UPJ). According to our operation, the result is fiber optic pressure sensing devices are highly accurate and sensitive. Under the same conditions, IRP varied among different renal calyces and UPJ (P < 0.05). IRP was lowest at 50 ml/min and highest at 150 ml/min (P < 0.05). Surgical position had a significant effect on IRP (P < 0.05). 12/14 Fr UAS had a lower IRP than 11/13 Fr UAS. Therefore fiber optic pressure sensing devices are more advantageous for IRP measurements. In ureteroscopy, the type of ureteral sheath, the surgical position, the perfusion flow rate, and the location of the measurement all affect the intrarenal pressure value.

Generalized method for distributed detection and quantification of cracks in bridges

The development of a generalized machine learning approach based on distributed detection and quantification of cracks by optical fibers is described in this article. A Brillouin scattering optical fiber sensor system was employed to develop, test, and verify the method. The main components of the approach described herein consist of an unsupervised crack identification module based on the iForest algorithm and a crack quantification component by the one-dimensional convolutional neural network method. The main attribute of this model is the versatility for application in various types of structures. The proposed method does not require further application-dependent training or calibration as long as the structural applications employ the same optical fiber type and installation adhesives. The effectiveness of the proposed method was verified by two experiments involving a 15-m steel beam in the laboratory and monitoring a twin set of 332-m-long, five-span continuous box girder concrete bridges. Regarding crack detection capabilities, it was possible to detect 107 out of 112 cracks in the laboratory beam and 20 out of the 21 in the bridges. The resolution of crack opening displacements for the steel beam and concrete bridges were 20.6 and 21.7 µm, respectively. The verification experiments further indicated the generality of the approach in applications to various types of structures and materials.

Development of optical fiber strain sensor system based on machine learning and polarization

Based on the principle that the polarization state of light propagating in a single-mode fiber changes with external strains, an optical fiber sensor system based on machine learning and polarization for multi-point strain measurement is proposed. To address the influence of the front sensor on the rear sensor and to minimize interference from unrelated inputs, we have employed a data processing method that constructs an individual neural network model for each sensor. This approach uses the polarization state of the reflected light of the sensors as the neural networks’ input and the sensors’ rotation angles as the output, training the designed neural networks for learning. The trained neural networks produce predicted outputs that demonstrate high consistency with the experimental data, achieving an average prediction accuracy of 99% on test data. These results validate the effectiveness of our sensor system and data processing method.

A Multi-Point Optical Fibre Sensor for Proton Therapy

As the technology to deliver precise and very high radiotherapeutic doses with narrow margins grows to better serve patients with complex radiotherapeutic needs, so does the need for sensors and sensor systems that can reliably deliver multi-point dose monitoring and dosimetry for enhanced safety and access. To address this need, we investigated a novel five-point scintillator system for simultaneously sampling points across a 74 MeV proton beam with a Hamamatsu 16-channel MPPC array. We studied the response across beam widths from 25 mm down to 5 mm in diameter and in multiple depths to observe beam penumbrae and output factors as well as depth–dose. We found through comparison to ionization chambers and radiochromic film that the array is capable of measurements accurate to within 8% in the centre of proton beams from 5 to 25 mm in diameter, and within 2% at 3.5 cm depth in water. The results from three trials are repeatable after calibration to within &lt;1%. Overall, the five optical fibre sensor system shows promise as a fast, multipoint relative dosimetry system.

Frequency Down-Converted and Sensitivity Enhanced Fiber Optic Sensing System Based on Multi-Passband Microwave Photonic Filter by Cascaded Fiber Optic Interferometers

Frequency Down-Converted and Sensitivity Enhanced Fiber Optic Sensing System Based on Multi-Passband Microwave Photonic Filter by Cascaded Fiber Optic Interferometers

Wide-Range Distributed Optical Fiber Sensing System Based on Dynamic Extension of Rayleigh Scattering Intensity Spectrum

Wide-Range Distributed Optical Fiber Sensing System Based on Dynamic Extension of Rayleigh Scattering Intensity Spectrum

Non-Contact Short-Term Heart Rate Variability Analysis Under Paced Respiration Based on a Robust Fiber Optic Sensor System

Non-Contact Short-Term Heart Rate Variability Analysis Under Paced Respiration Based on a Robust Fiber Optic Sensor System

CFRP Conical Grid Space Structure with Embedded Fiber Optics: Design, Manufacturing and Test

This article discloses the activity developed in the framework of the research project “GRID” aiming at the feasibility demonstration of a fiber optic sensing system (FOS), based on fiber Bragg gratings (FGB), embedded in the ribs of a conical grid structure demonstrator in composite material (CFRP), manufactured by means of dry robotic winding, liquid resin infusion and oven curing. This structure represents an optimized and highly efficient conical adapter for satellite applications that was designed under the same requirements of a conventional CFRP benchmark solution in order to evaluate possible mass savings. Specific interfaces were conceived in order to facilitate the insertion of the fiber optics in the center of helical ribs—pausing the automated deposition phase of the dry preform—and secure them to the structure. Representative grid articles were produced and tested to select the materials and evaluate the preliminary feasibility of the integrated system in conjunction with the infusion process. The proper functioning and use of the sensing system were finally proven during the various phases of the mechanical testing campaign of the demonstrator. Such a campaign included stiffness and strength evaluations and culminated with the catastrophic failure of the structure. The significant amount of data collected from several sensors embedded in the ribs and from conventional sensors glued outside the ribs helped us to better understand the structural behavior and to validate the design and analysis models. The main steps of the design, manufacturing and tests of this project are here addressed.

“Focus on sales by developing something unique”

AbstractEPIC's technology manager Antonio Castelo met Bjorn Andersen from a Danish developer of novel polymer optical fiber sensor systems.

Hierarchical Bayesian calibration of deck deflection models using distributed fiber optic strain data

A naval hovercraft was outfitted with a distributed fiber optic sensing system (FOSS) on its cargo deck in which two fibers were placed along orthogonal axes. A series of trucks were loaded onto the cargo deck to simulate operational loading conditions and the resulting strain profiles were measured by the distributed FOSS. The aim of this study is to quantify the deck deflections that correspond to the measured strains under different vehicular loads. This is accomplished by idealizing the cargo deck as a rectangular plate and using the data to estimate the associated plate model parameters. In order to account for measurement noise and model prediction error, as well as the inherent variability of inferences made with different data sets, a hierarchical Bayesian scheme that considers both the model parameter uncertainty and the prediction error variance is utilized to derive the posterior distributions of the model hyper-parameters, i.e., their mean and variance. Given the volume of FOSS strain data available, different approaches were taken for incorporating the data into the hierarchical approach in order to determine whether the inclusion of all available strain data meaningfully reduced the uncertainty compared to using only time-averaged strain. Comparisons between the different approaches are explored in the context of 95% confidence intervals for strain profiles, deck deflection envelopes, and reliability indices based on allowable deflections.