Hyperspectral vision based probe for in situ corrosion monitoring in saline environments
Corrosion is a major concern in various engineering fields. Early detection of corrosion helps in reducing the risk associated with the failure of metal structures invaded by corrosion. Though several techniques exist for monitoring corrosion, most of these techniques are contact-based and tend to b...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/164506 |
| _version_ | 1811687527797489664 |
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| author | Antony, Maria Merin Suchand Sandeep, Chandramathi Sukumaran Lim, Hoong-Ta Vadakke Matham, Murukeshan |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Antony, Maria Merin Suchand Sandeep, Chandramathi Sukumaran Lim, Hoong-Ta Vadakke Matham, Murukeshan |
| author_sort | Antony, Maria Merin |
| collection | NTU |
| description | Corrosion is a major concern in various engineering fields. Early detection of corrosion helps in reducing the risk associated with the failure of metal structures invaded by corrosion. Though several techniques exist for monitoring corrosion, most of these techniques are contact-based and tend to be destructive in nature. In addition, only a few of these techniques can provide fast, point-by-point analysis of corrosion. In this article, we propose and demonstrate a novel corrosion inspection probe with integrated illumination for the sensitive monitoring of corrosion along each point of an area under inspection based on snapshot hyperspectral (HS) vision. Being a small and flexible probe, it can be used for monitoring corrosion in difficult-to-access locations. The experimental results correlate well with the standard corrosion prediction model and are also validated by spectroscopic Raman analysis. The possibility of incorporating automated corrosion monitoring based on machine vision as the future development of the probe is also highlighted. |
| first_indexed | 2024-10-01T05:17:44Z |
| format | Journal Article |
| id | ntu-10356/164506 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:17:44Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1645062024-01-27T16:47:57Z Hyperspectral vision based probe for in situ corrosion monitoring in saline environments Antony, Maria Merin Suchand Sandeep, Chandramathi Sukumaran Lim, Hoong-Ta Vadakke Matham, Murukeshan School of Mechanical and Aerospace Engineering Centre for Optical and Laser Engineering Science::Physics::Optics and light Corrosion Hyperspectral Imaging Machine Vision Nondestructive Testing Probes Corrosion is a major concern in various engineering fields. Early detection of corrosion helps in reducing the risk associated with the failure of metal structures invaded by corrosion. Though several techniques exist for monitoring corrosion, most of these techniques are contact-based and tend to be destructive in nature. In addition, only a few of these techniques can provide fast, point-by-point analysis of corrosion. In this article, we propose and demonstrate a novel corrosion inspection probe with integrated illumination for the sensitive monitoring of corrosion along each point of an area under inspection based on snapshot hyperspectral (HS) vision. Being a small and flexible probe, it can be used for monitoring corrosion in difficult-to-access locations. The experimental results correlate well with the standard corrosion prediction model and are also validated by spectroscopic Raman analysis. The possibility of incorporating automated corrosion monitoring based on machine vision as the future development of the probe is also highlighted. Nanyang Technological University National Research Foundation (NRF) Singapore Food Agency Submitted/Accepted version This work was supported in part by the National Research Foundation, Singapore and the Singapore Food Agency, through its Singapore Food Story Research Development Programme (Theme 1: Sustainable Urban Food Production) under Grant SFS_RND_SUFP_001_03; and in part by COLE-EDB funding at the Centre for Optical and Laser Engineering, Nanyang Technological University. 2023-01-30T04:25:31Z 2023-01-30T04:25:31Z 2022 Journal Article Antony, M. M., Suchand Sandeep, C. S., Lim, H. & Vadakke Matham, M. (2022). Hyperspectral vision based probe for in situ corrosion monitoring in saline environments. IEEE Transactions On Instrumentation and Measurement, 71, 5026307-. https://dx.doi.org/10.1109/TIM.2022.3221115 0018-9456 https://hdl.handle.net/10356/164506 10.1109/TIM.2022.3221115 2-s2.0-85141594008 71 5026307 en SFS_RND_SUFP_001_03 IEEE Transactions on Instrumentation and Measurement © 2022 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/TIM.2022.3221115. application/pdf |
| spellingShingle | Science::Physics::Optics and light Corrosion Hyperspectral Imaging Machine Vision Nondestructive Testing Probes Antony, Maria Merin Suchand Sandeep, Chandramathi Sukumaran Lim, Hoong-Ta Vadakke Matham, Murukeshan Hyperspectral vision based probe for in situ corrosion monitoring in saline environments |
| title | Hyperspectral vision based probe for in situ corrosion monitoring in saline environments |
| title_full | Hyperspectral vision based probe for in situ corrosion monitoring in saline environments |
| title_fullStr | Hyperspectral vision based probe for in situ corrosion monitoring in saline environments |
| title_full_unstemmed | Hyperspectral vision based probe for in situ corrosion monitoring in saline environments |
| title_short | Hyperspectral vision based probe for in situ corrosion monitoring in saline environments |
| title_sort | hyperspectral vision based probe for in situ corrosion monitoring in saline environments |
| topic | Science::Physics::Optics and light Corrosion Hyperspectral Imaging Machine Vision Nondestructive Testing Probes |
| url | https://hdl.handle.net/10356/164506 |
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