Machine Learning For A Vernier-effect-based Optical Fiber Sensor

In recent years, the optical Vernier effect has been demonstrated as an effective tool to improve the sensitivity of optical fiber interferometer-based sensors, potentially facilitating a new generation of highly sensitive fiber sensing systems. Previous work has mainly focused on the physical implementation of Vernier-effect-based sensors using different combinations of interferometers, while the signal demodulation aspect has been neglected. However, accurate and reliable extraction of useful information from the sensing signal is critically important and determines the overall performance of the sensing system. In this Letter, we, for the first time, propose and demonstrate that machine learning (ML) can be employed for the demodulation of optical Vernier-effect-based fiber sensors. ML analysis enables direct, fast, and reliable readout of the measurand from the optical spectrum, avoiding the complicated and cumbersome data processing required in the conventional demodulation approach. This work opens new avenues for the development of Vernier-effect-based high-sensitivity optical fiber sensing systems

Simultaneous And Multiplexed Measurement Of Curvature And Strain Based On Optical Fiber Fabry-Perot Interferometric Sensors

Optical fiber sensors that have a compact size and the capability for multi-parameter sensing are desired in various applications. This article reports a miniaturized optical fiber Fabry-Perot interferometric sensor with a length of hundreds of µm that is able to simultaneously measure variations of curvature, temperature, and strain. The sensor is easy to fabricate, requiring only the fusion splicing of a short section of the silica capillary tube between two single-mode fibers (SMFs). The combined mechanism of the Fabry-Perot interference occurred in the two interfaces between the capillary and the SMFs, and the Anti resonant guidance induced by the capillary tube makes the device capable of realizing multi-parameter sensing. A simplified coefficient matrix approach is developed to decouple the contributions from different parameters. In addition, the capability of the device for multiplexing is investigated, where four such prototypes with different air cavity lengths are multiplexed in a system in parallel. The spectral behavior of an individual device for measuring curvature and strain is reconstructed and investigated, showing reliable responses and little crosstalk between different devices. The proposed device is easy to fabricate, cost-effective, robust, and could find potential applications in the field of structural health monitoring and medical and human–machine interactive sensing

Multi-Point Optical Fiber Fabry-Perot Curvature Sensor Based On Microwave Photonics

This article reports a multi-point curvature sensor system based on multiplexed optical fiber Fabry-Perot interferometric (FPI) sensor devices and a microwave photonics interrogation technique. The FPI sensor is fabricated with the assistance of a capillary tube, where a short section of the capillary is sandwiched between two single-mode fibers, forming the airgap Fabry-Perot cavity. Bending of the FPI device leads to changes in the fringe contrast of its reflection spectrum. Based on the microwave photonics filtering technique, variations of the fringe contrast are encoded into the changes in the peak magnitude of the passband in the frequency response of the FPI device. By multiplexing such FPI devices with different cavity lengths, multi-point measurements of curvature can be realized by tracking changes in corresponding passbands in the frequency response of the system. The FPI curvature sensor is easy-to-manufacture and cost-effective, and the microwave photonics-based system provides an alternative and robust solution to interrogating the multiplexed FPI sensors for multi-point curvature sensing that could be desired in structural health monitoring, human-machine interface sensing, and other related fields

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

In This Paper, We Report an Array of Fiber-Optic Sensors based on the Fabry-Perot Interference Principle and Machine Learning-Based Analyses for Identifying Volatile Organic Liquids (VOLs). Three Optical Fiber Tip Sensors with Different Surfaces Were Included in the Array of Sensors to Improve the Accuracy for Identifying Liquids: An Intrinsic (Unmodified) Flat Cleaved End face, a Hydrophobic-Coated End face, and a Hydrophilic-Coated End face. the Time-Transient Responses of Evaporating Droplets from the Optical Fiber Tip Sensors Were Monitored and Collected Following the Controlled Immersion Tests of 11 Different Organic Liquids. a Continuous Wavelet Transform Was Used to Convert the Time-Transient Response Signal into Images. These Images Were Then Utilized to Train Convolution Neural Networks for Classification (Identification of VOLs). We Show that Diversity in the Information Collected using the Array of Three Sensors Helps Machine Learning-Based Methods Perform Significantly Better. We Explore Different Pipelines for Combining the Information from the Array of Sensors within a Machine Learning Framework and their Effect on the Robustness of Models. the Results Showed that the Machine Learning-Based Methods Achieved High Accuracy in their Classification of Different Liquids based on their Droplet Evaporation Time-Transient Events

Chemical Classification By Monitoring Liquid Evaporation Using Extrinsic Fabry-Perot Interferometer With Microwave Photonics

Identification of liquids is essential in chemical analysis, safety, environmental protection, quality control, and research. A novel liquid identification system based on Microwave Photonics (MWP) measured time transient evaporation signals is investigated. An extrinsic Fabry-Perot Interferometer (EFPI) based optical probe using single-mode fiber (SMF) is proposed to monitor evaporation of different liquids. The MWP system is used to measure the optical path changes during liquid evaporation due to its high sensitivity, selectivity, and Signal-to-Noise Ratio (SNR). The measured S21 continuous wave (CW) time Magnitude and Phase signals were processed to extract features such as histogram and Fast Fourier Transform (FFT) peaks. Using features extracted from droplet evaporation time transient events, machine learning classification accurately identified chemicals in each liquid with an accuracy rate of over 99%, employing three algorithms: Decision Trees, Support Vector Machine (SVM), and K-nearest neighbors (KNN). The classification results demonstrate accurate liquid identification based on evaporation measurements by the MWP system

Machine Learning Identifies Liquids Employing a Simple Fiber-Optic Tip Sensor

We proposed an extremely simple fiber-optic tip sensor system to identify liquids by combining their corresponding droplet evaporation events with analyses using machine learning techniques. Pendant liquid droplets were suspended from the cleaved endface of a single-mode fiber during the experiment. The optical fiber-droplet interface and the droplet-air interface served as two partial reflectors of an extrinsic Fabry-Perot interferometer (EFPI) with a liquid droplet cavity. As the liquid pendant droplet evaporated, its length diminished. A light source can be used to observe the effective change in the net reflectivity of the optical fiber sensor system by observing the resulting optical interference phenomenon of the reflected waves. Using a single-wavelength probing light source, the entire evaporation event of the liquid droplet was precisely captured. The measured time transient response from the fiber-optic tip sensor to an evaporation event of a liquid droplet of interest was then transformed into image data using a continuous wavelet transform. The obtained image data was used to fine-tune pre-trained convolution neural networks (CNNs) for the given task. The results demonstrated that machine learning-based classification methods achieved greater than 98% accuracy in classifying different liquids based on their corresponding droplet evaporation processes, measured by the fiber-optic tip sensor

Optical Fiber Tip Learns to Identify Liquids

We Propose and Demonstrate an Extremely Simple Fiber-Optic Tip Sensor System to Identify Liquids through Droplet Evaporation Events on the Fiber Tip, Analyzed using Machine Learning Techniques