A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Fracture Behavior of Epoxy Asphalt Pavement on Steel Bridges based on Optical Fiber Sensing Technology and Numerical Simulation

The distributed optical fiber sensing technology was used to investigate the fracture behavior of the Epoxy Asphalt Mixture. The spatial distribution and variation of the strain development with crack propagation were acquired using the brillouin optical time-domain reflectometer through the loading experiments of the composite beam structure. In addition, a finite element model of the composite beam structure was developed to analyze the mechanical responses of the epoxy asphalt mixture using the extended finite element method. The experimental results show that the development of crack propagation becomes instable with the increase of the load, and larger loads will generate deeper cracks. Moreover, the numerical results show that the mechanical response of the crack tip changes with the crack propagation, and the worst areas that subjected to crack damage are located on both sides of the composite beam structure.

Integrated multi-mode glass ceramicfiber for high-resolution temperature sensing

Optical temperature sensors,which can accurately detect temperature in biological systems,are crucial to the development of healthcare monitoring.To challenge the state-of-art technology,it is necessary to design single luminescence center doped materials with multi-wavelength emission for optical temperature sensors with more modes and higher resolution.Here,an Er^(3+)single-doped KYF4 nanocrystals glass ceramic with an obvious thermochromic phenomenon is reported for thefirst time,which shows a different temperature-dependent green,red,and near-infrared luminescence behavior based on thermal disturbance model.In addition,Er^(3+)single-doped GCfiber was drawn and fabricated into multi-mode opticalfiber temperature sensor,which has superior measured temperature resolution(<0.5℃),excellent detection limit(0.077℃),and high correlation coefficient(R^(2))of 0.99997.More importantly,this sensor can monitor temperature in different scenarios with great environmental interference resistance and repeatability.These results indicate that our sensor shows great promise as a technology for environmental and healthcare monitoring,and it provides a route for the design of opticalfiber temperature sensors with multi-mode and high resolution.

Detection of Fuel Adulteration Using Wave Optical with Machine Learning Algorithms

Fuel is a very important factor and has considerable influence on the air quality in the environment,which is the heart of the world.The increase of vehi-cles in lived-in areas results in greater emission of carbon particles in the envir-onment.Adulterated fuel causes more contaminated particles to mix with breathing air and becomes the main source of dangerous pollution.Adulteration is the mixing of foreign substances in fuel,which damages vehicles and causes more health problems in living beings such as humans,birds,aquatic life,and even water resources by emitting high levels of hydrocarbons,nitrogen oxides,and carbon monoxide.Most frequent blending liquids are lubricants and kerosene in the petrol,and its adulteration is a considerable problem that adds to environ-mental pollution.This study focuses on detecting the adulteration in petrol using sensors and machine learning algorithms.A modified evanescent wave opticalfiber sensor with discrete wavelet transform is proposed for classification of adult-erated data from the samples.Furthermore,support vector machine classifier is used for accurate categorization.The sensor isfirst tested with fuel and numerical data is classified based on machine learning algorithms.Finally,the result is eval-uated with less error and high accuracy of 99.9%,which is higher than all existing techniques.