Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study
This study investigates a self-referencing method for damage detection and localization using guided waves (GW) sensed by fiber Bragg grating (FBG) sensors. The research integrates advanced numerical simulations with an innovative configuration of sensors to enhance structural health monitoring (SH...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2025-07-01
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| Series: | Computer Assisted Methods in Engineering and Science |
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| Online Access: | https://cames.ippt.pan.pl/index.php/cames/article/view/1805 |
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| author | Abhishek Patange Farzam Omidi Moaf Piotr Fiborek Adityan Arumuganainar Rohan Nandkishor Soman |
| author_facet | Abhishek Patange Farzam Omidi Moaf Piotr Fiborek Adityan Arumuganainar Rohan Nandkishor Soman |
| author_sort | Abhishek Patange |
| collection | DOAJ |
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This study investigates a self-referencing method for damage detection and localization using guided waves (GW) sensed by fiber Bragg grating (FBG) sensors. The research integrates advanced numerical simulations with an innovative configuration of sensors to enhance structural health monitoring (SHM). A self-referencing setup, employing FBG sensors with edge filtering method and remote bonding, enables a baseline-free damage detection approach. The methodology is validated as a proof-of-concept numerical model. The simulation framework incorporates a three-dimensional spectral element method for precise and efficient modelling of GW propagation and interactions with structural anomalies. Three different machine learning (ML) techniques are employed to detect and localize damages, demonstrating effectiveness of ML methods compared to traditional methods.
The three techniques employed are decision tree, logistic model tree and random forest. Key findings highlight the effectiveness of random forest models in classifying damage states with a 98.67% accuracy. Different feature selection methods, are used to identify critical features. The proposed methodology reduces sensor requirements, lowers system complexity and cost, and enables efficient SHM solutions in extreme or large-scale environments. This work underscores the potential of ML techniques to perform detection and localization where traditional techniques fail.
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| format | Article |
| id | doaj-art-08abfd9b1bd142aab2e625c2fce4e5a9 |
| institution | Kabale University |
| issn | 2299-3649 2956-5839 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Computer Assisted Methods in Engineering and Science |
| spelling | doaj-art-08abfd9b1bd142aab2e625c2fce4e5a92025-08-20T03:50:06ZengInstitute of Fundamental Technological Research Polish Academy of SciencesComputer Assisted Methods in Engineering and Science2299-36492956-58392025-07-0132210.24423/cames.2025.1805Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical StudyAbhishek Patange0Farzam Omidi Moaf1Piotr Fiborek2Adityan Arumuganainar3Rohan Nandkishor Soman4School of Mechanical Engineering, Vellore Institute of Technology, ChennaiInstitute of Fluid Flow Machinery, Polish Academy of Sciences, GdańskInstitute of Fluid Flow Machinery, Polish Academy of Sciences, GdańskCOEP Technological University, Shivajinagar, Pune, MaharashtraInstitute of Fluid Flow Machinery, Polish Academy of Sciences, Gdańsk This study investigates a self-referencing method for damage detection and localization using guided waves (GW) sensed by fiber Bragg grating (FBG) sensors. The research integrates advanced numerical simulations with an innovative configuration of sensors to enhance structural health monitoring (SHM). A self-referencing setup, employing FBG sensors with edge filtering method and remote bonding, enables a baseline-free damage detection approach. The methodology is validated as a proof-of-concept numerical model. The simulation framework incorporates a three-dimensional spectral element method for precise and efficient modelling of GW propagation and interactions with structural anomalies. Three different machine learning (ML) techniques are employed to detect and localize damages, demonstrating effectiveness of ML methods compared to traditional methods. The three techniques employed are decision tree, logistic model tree and random forest. Key findings highlight the effectiveness of random forest models in classifying damage states with a 98.67% accuracy. Different feature selection methods, are used to identify critical features. The proposed methodology reduces sensor requirements, lowers system complexity and cost, and enables efficient SHM solutions in extreme or large-scale environments. This work underscores the potential of ML techniques to perform detection and localization where traditional techniques fail. https://cames.ippt.pan.pl/index.php/cames/article/view/1805guided waves (GW)fiber Bragg grating (FBG) sensorsdamage detectionself-referencing methodmachine learning (ML) referencing methodnumerical simulation |
| spellingShingle | Abhishek Patange Farzam Omidi Moaf Piotr Fiborek Adityan Arumuganainar Rohan Nandkishor Soman Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study Computer Assisted Methods in Engineering and Science guided waves (GW) fiber Bragg grating (FBG) sensors damage detection self-referencing method machine learning (ML) referencing method numerical simulation |
| title | Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study |
| title_full | Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study |
| title_fullStr | Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study |
| title_full_unstemmed | Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study |
| title_short | Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study |
| title_sort | damage localization using fiber bragg grating sensors in self referencing conguration a numerical study |
| topic | guided waves (GW) fiber Bragg grating (FBG) sensors damage detection self-referencing method machine learning (ML) referencing method numerical simulation |
| url | https://cames.ippt.pan.pl/index.php/cames/article/view/1805 |
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