Development of an intelligent data analytics system for real-time condition monitoring of gas pipes
Objective: Gas pipes are laid underground where soil texture and temperature changes. The Fiber Bragg Grating sensor is kind of sensor which can get first-hand reliable data from complicated situation. Methods: Doing FBG sensor experiment (especially the data of the temperature and strain) to collec...
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sg-ntu-dr.10356-662852023-07-04T15:03:39Z Development of an intelligent data analytics system for real-time condition monitoring of gas pipes Wang, Zuyuan So Ping Lam School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Objective: Gas pipes are laid underground where soil texture and temperature changes. The Fiber Bragg Grating sensor is kind of sensor which can get first-hand reliable data from complicated situation. Methods: Doing FBG sensor experiment (especially the data of the temperature and strain) to collect FBG data of sensor network of FBG sensing system. And using Artificial Neural Network to analyze the data to get the relation between the measurand and the wavelength shift. Results: Verify the dual sensitivity of FBG sensors to temperature and strain. Extension guesses about the three-sensitivity of FBG sensors to temperature, strain and acoustic effect. Build up BP training neural network with good performance.Conclusion: FBG sensor is an ideal senor in monitoring of gas-pipe and it is in the development of the present age. Artificial Neural Network is a good approach to analyze the monitor data of FBG system. Master of Science (Power Engineering) 2016-03-23T03:31:25Z 2016-03-23T03:31:25Z 2016 Thesis http://hdl.handle.net/10356/66285 en 68 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Wang, Zuyuan Development of an intelligent data analytics system for real-time condition monitoring of gas pipes |
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Objective: Gas pipes are laid underground where soil texture and temperature changes. The Fiber Bragg Grating sensor is kind of sensor which can get first-hand reliable data from complicated situation. Methods: Doing FBG sensor experiment (especially the data of the temperature and strain) to collect FBG data of sensor network of FBG sensing system. And using Artificial Neural Network to analyze the data to get the relation between the measurand and the wavelength shift. Results: Verify the dual sensitivity of FBG sensors to temperature and strain. Extension guesses about the three-sensitivity of FBG sensors to temperature, strain and acoustic effect. Build up BP training neural network with good performance.Conclusion: FBG sensor is an ideal senor in monitoring of gas-pipe and it is in the development of the present age. Artificial Neural Network is a good approach to analyze the monitor data of FBG system. |
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So Ping Lam |
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So Ping Lam Wang, Zuyuan |
format |
Theses and Dissertations |
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Wang, Zuyuan |
author_sort |
Wang, Zuyuan |
title |
Development of an intelligent data analytics system for real-time condition monitoring of gas pipes |
title_short |
Development of an intelligent data analytics system for real-time condition monitoring of gas pipes |
title_full |
Development of an intelligent data analytics system for real-time condition monitoring of gas pipes |
title_fullStr |
Development of an intelligent data analytics system for real-time condition monitoring of gas pipes |
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Development of an intelligent data analytics system for real-time condition monitoring of gas pipes |
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development of an intelligent data analytics system for real-time condition monitoring of gas pipes |
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2016 |
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http://hdl.handle.net/10356/66285 |
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