Polymer optical fiber specklegram strain sensor with extended dynamic range

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORA polymer optical fiber strain sensor with extended dynamic range is reported. The proposed algorithm resets the reference fiber status depending on the magnitude of the specklegram deviation so the correlation coefficient never saturates, yielding a continuous response over the full range for both positive and negative strains. The technique was evaluated on the measurement of axial strains using a ZEONEX core, poly(methyl methacrylate) cladding multimode fiber, presenting reproducible results with 3 x 10(-3) mu epsilon(-1) sensitivity (similar to 15 mu epsilon resolution) within a 22,600 mu epsilon interval. In contrast to the available approaches, the presented method can retrieve the strain direction and does not require intensive image processing, thus providing a simple and reliable technique for mechanical measurements using multimode optical fibers. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)571119FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIOR2014/50632-62017/25666-2sem informaçãosem informaçã

A Machine Learning Specklegram Wavemeter (MaSWave) Based On A Short Section Of Multimode Fiber As The Dispersive Element

Wavemeters are very important for precise and accurate measurements of both pulses and continuous-wave optical sources. Conventional wavemeters employ gratings, prisms, and other wavelength-sensitive devices in their design. Here, we report a simple and low-cost wavemeter based on a section of multimode fiber (MMF). The concept is to correlate the multimodal interference pattern (i.e., speckle patterns or specklegrams) at the end face of an MMF with the wavelength of the input light source. Through a series of experiments, specklegrams from the end face of an MMF as captured by a CCD camera (acting as a low-cost interrogation unit) were analyzed using a convolutional neural network (CNN) model. The developed machine learning specklegram wavemeter (MaSWave) can accurately map specklegrams of wavelengths up to 1 pm resolution when employing a 0.1 m long MMF. Moreover, the CNN was trained with several categories of image datasets (from 10 nm to 1 pm wavelength shifts). In addition, analysis for different step-index and graded-index MMF types was carried out. The work shows how further robustness to the effects of environmental changes (mainly vibrations and temperature changes) can be achieved at the expense of decreased wavelength shift resolution, by employing a shorter length MMF section (e.g., 0.02 m long MMF). In summary, this work demonstrates how a machine learning model can be used for the analysis of specklegrams in the design of a wavemeter

Numerical study using finite element method for the thermal response of fiber specklegram sensors with changes in the length of the sensing zone

The response of fiber specklegram sensors (FSSs) is given as function of variations in the intensity distribution of the modal interference pattern or speckle pattern induced by external disturbances. In the present work, the behavior of a FSS sensing scheme under thermal perturbations is studied by means of computational simulations of the speckle patterns. These simulations are generated by applying the finite element method (FEM) to the modal interference in optical fibers as a function of the thermal disturbance and the length of the sensing zone. A correlation analysis is performed on the images generated in the simulations to evaluate the dependence between the changes in the speckle pattern grains and the intensity of the applied disturbance. The numerical simulation shows how the building characteristic of the length of sensing zone, combined with image processing, can be manipulated to control the metrological performance of the sensors.This work was partially funded by the Instituto Tecnológico Metropolitano (grant P20215), the Politécnico Jaime Isaza Cadavid (grant 2020/00132/001) and the Universidad Cooperativa de Colombia (grant INV2903). Y.A. Vélez also thanks the support given to her by the Instituto Tecnológico Metropolitano through its program of young researchers

Machine learning for quantitative fibre optic sensing

Optical fibre sensing correlates measurable properties of the light guided within an optical fibre with an external parameter being sensed. There are a number of different methods of fibre sensing, including scattering based sensing, specklegram sensing and interferometric sensing, each with their own applications and limitations. Sensing with multimode fibre offers some significant advantages over single mode fibre, but carries the inherent limitation of extracting useful information from the highly complex and sensitive process of multimode fibre transmission. Deep learning is a form of machine learning at the forefront of data analysis and processing which has solved many problems in a wide range of applications, most notably image and speech recognition. Its application in multimode fibre imaging and sensing has been brief but successful. In this thesis, deep learning is explored as a tool for understanding and quantifying the complex multimode fibre transmission process for sensing applications. Chapter 2 looks at deep learning applied to fibre specklegram sensing, demonstrating its ability to correlate the change in the specklegram output of the fibre with a parameter of the fibre's environment for temperature and refractive index sensing. The superiority of the deep learning approach over current statistical methods is demonstrated, as the deep neural networks improve upon the issues of limited dynamic range and vulnerability to specklegram misalignment that are present with the correlation method. At the same time, the first example of deep learning for regression-based sensing of a continuous variable, as opposed to discrete/classification sensing, is presented. Chapter 3 looks at deep learning applied to sensing with the wavelength spectrum output of a multimode fibre. Current methods of interferometric sensing with a wavelength spectrum and multimode fibre involve the need to inscribe resonance-producing structures within the fibre, which can be costly and time-consuming. The use of deep learning has been previously explored as a method of extracting information pertaining to an environmental parameter, specifically temperature, from a wavelength spectrum without the presence of any resonant features. This thesis looks to build upon such work by demonstrating multi-point sensing using the concept of encoding spatially resolved temperature information in a wavelength spectrum. Sapphire crystal optical fibre is used for sensing, where its highly variable fibre radius is exploited as a means of encoding such spatially resolved information in a fashion that a deep neural network can learn. It is shown that such networks trained on spectra from sapphire fibre perform far better for multi-point temperature sensing than those trained on spectra from glass silica fibres of constant radius.Thesis (Ph.D.) -- University of Adelaide, School of Physical Sciences, 202

Plastic Optical Fiber Displacement Sensor Based on Dual Cycling Bending

In this study, a high sensitivity and easy fabricated plastic optical fiber (POF) displacement sensor is proposed. A POF specimen subjected to dual cyclic bending is used to improve the sensitivity of the POF displacement sensor. The effects of interval between rollers, relative displacement and number of rollers on the sensitivity of the displacement sensor are analyzed both experimentally and numerically. A good agreement between the experimental measurements and numerical calculations is obtained. The results show that the interval between rollers affects sensitivity most significantly than the other design parameters. Based on the experimental data, a linear equation is derived to estimate the relationship between the power loss and the relative displacement. The difference between the estimated results and the experimental results is found to be less than 8%. The results also show that the proposed POF displacement sensor based on dual cyclic bending can be used to detect displacement accurately

Bibliography of Lewis Research Center technical publications announced in 1986

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1986. All the publications were announced in the 1986 issues of Scientific and Technical Aerospace Reports (STAR) and/or International Aerospace Abstracts (IAA). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses