Fiber-optic system for monitoring stability of quarry slopes
This paper presents issues related to the development of a system for monitoring the displacement of the mountain range leading to the collapse of the sides of the quarry. The control system uses chiseled fiber-optic sensors. Fiberoptic sensors are based on Single Mode optical fibers. A laborato...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2022-11-01
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| Series: | Омский научный вестник |
| Subjects: | |
| Online Access: | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2022/%E2%84%96%204%20(184)%20(%D0%9E%D0%9D%D0%92)/112-117%20%D0%9C%D0%B0%D0%B4%D0%B8%20%D0%9F.%20%D0%A8.,%20%D0%90%D0%BB%D1%8C%D0%BA%D0%B8%D0%BD%D0%B0%20%D0%90.%20%D0%94.,%20%D0%AE%D1%80%D1%87%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%90.%20%D0%92.,%20%D0%9C%D0%B5%D1%85%D1%82%D0%B8%D0%B5%D0%B2%20%D0%90.%20%D0%94.,%20%D0%90%D0%B9%D0%BC%D0%B0%D0%B3%D0%B0%D0%BC%D0%B1%D0%B5%D1%82%D0%BE%D0%B2%D0%B0%20%D0%A0.%20%D0%96..pdf |
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| Summary: | This paper presents issues related to the development of
a system for monitoring the displacement of the mountain
range leading to the collapse of the sides of the quarry.
The control system uses chiseled fiber-optic sensors. Fiberoptic sensors are based on Single Mode optical fibers. A
laboratory sample of a point fiber-optic sensor based on a
two-shoulder Mach-Zehnder interferometer using a Single
Mode optical fiber for displacement control has been
developed. The simulation stand of a laboratory sample of
a fiber-optic sensor showed high measurement accuracy,
linearity and can be used to control the stability of the
sides of the quarry after the design has been improved.
The experimental data obtained are processed using the
Wolfram Alpha computer program. A hardware-software
control complex has also been developed with a wide range
of elements that allows you to adjust sensitivity and has
machine learning elements. |
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| ISSN: | 1813-8225 2541-7541 |