Rayleigh-Based Distributed Optical Fiber Sensing

The Impact of 1030 nm fs-Pulsed Laser on Enhanced Rayleigh Scattering in Optical Fibers

This article presents a comprehensive study on the impact of irradiation optical fiber cores with a femtosecond-pulsed laser, operating at a wavelength of 1030 nm, on the signal amplitude in Rayleigh scattering-based optical frequency domain reflectometry (OFDR). The experimental study involves two fibers with significantly different levels of germanium doping: the standard single-mode fiber (SMF-28) and the ultra-high numerical aperture fiber (UHNA7). The research findings reveal distinct characteristics of reflected and scattered light amplitudes as a function of pulse energy. Although different amplitude changes are observed for the examined fibers, both can yield an enhancement of amplitude. The paper further investigates the effect of fiber Bragg grating inscription on the overall amplitude of reflected light. The insights gained from this study could be beneficial for controlling the enhancement of light scattering amplitude in fibers with low or high levels of germanium doping.

Distributed Temperature Sensing through Network Analysis Frequency-Domain Reflectometry

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to ≈3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal formed by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of attaining high measurand resolution (≈50 mK at 3-cm spatial resolution) thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, unlike the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser and therefore is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double-sideband or single-sideband modulated probe light. The results confirm the validity of the proposed approach.

Bite Force Mapping Based on Distributed Fiber Sensing Network Approach

Bite force measurements are crucial in the realm of biomedical research, particularly in the areas of dentistry and orthodontic care. Various intraoral devices have been used to assess biting force, but each has limitations and drawbacks. Fiber optic sensors (FOSs) offer advantages such as electrical inertness, immunity to electromagnetic interference, and high sensitivity. Distributed fiber optic sensing allows an increase in the number of sensing points and can interrogate numerous reflections from scattering events within an optical fiber. We present four dental bites with heights of 6 mm, which enabled bilateral measurements. U-shaped sensors were prepared by embedding fibers into silicone by folding a single-mode fiber into four lines and multiplexing eight parallel nanoparticle-doped fibers. Dental bite models were created using two silicone materials (Sorta Clear 18 and Sorta Clear 40). The developed sensors were calibrated by applying weights up to 900 g, resulting in a linear response. Experiments were conducted to compare the efficacy of the dental bites. The collection of massive data was enabled by constructing a 2D map of the dental bites during multi-point sensing.

A mode-locked random laser generating transform-limited optical pulses

Ever since the mid-1960's, locking the phases of modes enabled the generation of laser pulses of duration limited only by the uncertainty principle, opening the field of ultrafast science. In contrast to conventional lasers, mode spacing in random lasers is ill-defined because optical feedback comes from scattering centres at random positions, making it hard to use mode locking in transform limited pulse generation. Here the generation of sub-nanosecond transform-limited pulses from a mode-locked random fibre laser is reported. Rayleigh backscattering from decimetre-long sections of telecom fibre serves as laser feedback, providing narrow spectral selectivity to the Fourier limit. The laser is adjustable in pulse duration (0.34-20 ns), repetition rate (0.714-1.22 MHz) and can be temperature tuned. The high spectral-efficiency pulses are applied in distributed temperature sensing with 9.0 cm and 3.3 × 10-3 K resolution, exemplifying how the results can drive advances in the fields of spectroscopy, telecommunications, and sensing.

Distributed fiber optic shape sensing with simultaneous interrogation of multiple fibers based on Rayleigh-signature domain multiplexing

We propose a method for shape sensing that employs Rayleigh-signature domain multiplexing to simultaneously probe the fibers or cores of a shape sensing setup with a single optical frequency-domain reflectometry scan. The technique enables incrementing the measurement speed by a factor equal to the number of multiplexed fibers at the expense of an increased noise floor in accordance with the Cramér-Rao lower bound. Nonetheless, we verify that the shape reconstruction performance of the proposed method is in very good agreement with that of conventional sequential core interrogation.

A Phase-Sensitive Optical Time Domain Reflectometry with Non-Uniform Frequency Multiplexed NLFM Pulse

In the domain of optical fiber distributed acoustic sensing, the persistent challenge of extending sensing distances while concurrently improving spatial resolution and frequency response range has been a complex endeavor. The amalgamation of pulse compression and frequency division multiplexing methodologies has provided certain advantages. Nevertheless, this approach is accompanied by the drawback of significant bandwidth utilization and amplified hardware investments. This study introduces an innovative distributed optical fiber acoustic sensing system aimed at optimizing the efficient utilization of spectral resources by combining compressed pulses and frequency division multiplexing. The system continuously injects non-linear frequency modulation detection pulses spanning various frequency ranges. The incorporation of non-uniform frequency division multiplexing augments the vibration frequency response spectrum. Additionally, nonlinear frequency modulation adeptly reduces crosstalk and enhances sidelobe suppression, all while maintaining a favorable signal-to-noise ratio. Consequently, this methodology substantially advances the spatial resolution of the sensing system. Experimental validation encompassed the multiplexing of eight frequencies within a 120 MHz bandwidth. The results illustrate a spatial resolution of approximately 5 m and an expanded frequency response range extending from 1 to 20 kHz across a 16.3 km optical fiber. This achievement not only enhances spectral resource utilization but also reduces hardware costs, making the system even more suitable for practical engineering applications.

High resolution seafloor thermometry for internal wave and upwelling monitoring using Distributed Acoustic Sensing

Temperature is an essential oceanographic variable (EOV) that still today remains coarsely resolved below the surface and near the seafloor. Here, we gather evidence to confirm that Distributed Acoustic Sensing (DAS) technology can convert tens of kilometer-long seafloor fiber-optic telecommunication cables into dense arrays of temperature anomaly sensors having millikelvin (mK) sensitivity, thus allowing to monitor oceanic processes such as internal waves and upwelling with unprecedented detail. Notably, we report high-resolution observations of highly coherent near-inertial and super-inertial internal waves in the NW Mediterranean sea, offshore of Toulon, France, having spatial extents of a few kilometers and producing maximum thermal anomalies of more than 5 K at maximum absolute rates of more than 1 K/h. We validate our observations with in-situ oceanographic sensors and an alternative optical fiber sensing technology. Currently, DAS only provides temperature changes estimates, however practical solutions are outlined to obtain continuous absolute temperature measurements with DAS at the seafloor. Our observations grant key advantages to DAS over established temperature sensors, showing its transformative potential for the description of seafloor temperature fluctuations over an extended range of spatial and temporal scales, as well as for the understanding of the evolution of the ocean in a broad sense (e.g. physical and ecological). Diverse ocean-oriented fields could benefit from the potential applications of this fast-developing technology.

Polarization Properties of Coherently Superposed Rayleigh Backscattered Light in Single-Mode Fibers

The properties of the state of polarization (SOP) and the degree of polarization (DOP) of Rayleigh backscattered light (RBL) in single-mode fibers (SMF) are investigated theoretically and experimentally when the incident probe is a perfectly coherent continuous-wave (CW) light. It is concluded that the instantaneous DOP of the coherently superposed RBL is always 100%, and the instantaneous SOP is determined by the distributions of the birefringence and the optical phase along the SMF. Therefore, the instantaneous SOP of the coherently superposed RBL does not have a constant relationship with the SOP of the incident CW probe. Furthermore, the instantaneous SOP varies randomly with time because the optical phase is very sensitive to ambient temperature and vibration even in the lab environment. Further theoretical derivation and experimental verification demonstrate, for the first time, that the temporally averaged SOP of the coherently superposed RBL has a simple constant relationship with the SOP of the incident CW probe, and the temporally averaged DOP is 1/3 in an SMF with low and randomly distributed birefringence. The derived formulas and obtained findings can be used to enhance the modelling and improve the performances of phase-sensitive optical time-domain reflectometry and other Rayleigh backscattering based fiber-optic sensors.

Hybrid Distributed Optical Fiber Sensor for the Multi-Parameter Measurements

Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications.

Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system's cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.

Distributed optical strain sensing measurements down to cryogenic temperatures

Rayleigh backscattering (RBS)-based distributed fiber sensors technology is becoming more and more crucial in various fields such as aerospace and defense, automotive, civil, and geotechnical. This technology is measuring the naturally occurring Rayleigh backscatter level in the optical fiber core; thus, any standard single-mode telecom optical fiber can be used. The application of distributed optical fiber strain sensing in the harsh environments of the European Organization for Nuclear Research required several mechanical tests to study the accuracy of strain sensing in cryogenic conditions. This study compares the performance of a RBS-based distributed optical fiber strain sensing down to cryogenic temperatures (4.2 K) with previously validated instrumentations such as electrical strain gauges and fiber Bragg grating technologies.

A Review on Photonic Sensing Technologies: Status and Outlook

In contemporary science and technology, photonic sensors are essential. They may be made to be extremely resistant to some physical parameters while also being extremely sensitive to other physical variables. Most photonic sensors may be incorporated on chips and operate with CMOS technology, making them suitable for use as extremely sensitive, compact, and affordable sensors. Photonic sensors can detect electromagnetic (EM) wave changes and convert them into an electric signal due to the photoelectric effect. Depending on the requirements, scientists have found ways to develop photonic sensors based on several interesting platforms. In this work, we extensively review the most generally utilized photonic sensors for detecting vital environmental parameters and personal health care. These sensing systems include optical waveguides, optical fibers, plasmonics, metasurfaces, and photonic crystals. Various aspects of light are used to investigate the transmission or reflection spectra of photonic sensors. In general, resonant cavity or grating-based sensor configurations that work on wavelength interrogation methods are preferred, so these sensor types are mostly presented. We believe that this paper will provide insight into the novel types of available photonic sensors.

A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.

Machine Learning Estimation of the Phase at the Fading Points of an OFDR-Based Distributed Sensor

The paper reports a machine learning approach for estimating the phase in a distributed acoustic sensor implemented using optical frequency domain reflectometry, with enhanced robustness at the fading points. A neural network configuration was trained using a simulated set of optical signals that were modeled after the Rayleigh scattering pattern of a perturbed fiber. Firstly, the performance of the network was verified using another set of numerically generated scattering profiles to compare the achieved accuracy levels with the standard homodyne detection method. Then, the proposed method was tested on real experimental measurements, which indicated a detection improvement of at least 5.1 dB with respect to the standard approach.