Autonomous monitoring and fault detection for electrical motor driven belt system
Electrical motor driven belt systems are extensively employed in airport, seaport and industrial plants. Despite of their strong cargo handling capability, mechanical faults are regularly experienced in such systems. Nowadays, the monitoring and detection of mechanical faults in electrical motor dri...
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sg-ntu-dr.10356-785682023-07-04T16:18:22Z Autonomous monitoring and fault detection for electrical motor driven belt system Li, Hongren Zhang Xinan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Electrical motor driven belt systems are extensively employed in airport, seaport and industrial plants. Despite of their strong cargo handling capability, mechanical faults are regularly experienced in such systems. Nowadays, the monitoring and detection of mechanical faults in electrical motor driven belt system still depend on the on-site examination by professional staff. Nevertheless, this type of fault detection is very expensive. To solve this problem, this project proposes to design an effective and autonomous monitoring and fault detection algorithm for electrical motor driven belt system. The innovation of this algorithm is that no sensor is needed. It contributes to greatly reduce the cost and improve the efficiency of fault detection. The algorithm proposed in this project has been implemented in Matlab & Simulink to verify the method. The result of this project shows that the fault can be detected automatically and the precise parameters can be traced by using the proposed algorithm. In the future, the algorithm will be applied to the specific motor system for simulation and experiment. How to reduce the failure detection time will also become the research content of the next stage. Master of Science (Power Engineering) 2019-06-24T01:54:21Z 2019-06-24T01:54:21Z 2019 Thesis http://hdl.handle.net/10356/78568 en 65 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Li, Hongren Autonomous monitoring and fault detection for electrical motor driven belt system |
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Electrical motor driven belt systems are extensively employed in airport, seaport and industrial plants. Despite of their strong cargo handling capability, mechanical faults are regularly experienced in such systems. Nowadays, the monitoring and detection of mechanical faults in electrical motor driven belt system still depend on the on-site examination by professional staff. Nevertheless, this type of fault detection is very expensive. To solve this problem, this project proposes to design an effective and autonomous monitoring and fault detection algorithm for electrical motor driven belt system. The innovation of this algorithm is that no sensor is needed. It contributes to greatly reduce the cost and improve the efficiency of fault detection. The algorithm proposed in this project has been implemented in Matlab & Simulink to verify the method. The result of this project shows that the fault can be detected automatically and the precise parameters can be traced by using the proposed algorithm. In the future, the algorithm will be applied to the specific motor system for simulation and experiment. How to reduce the failure detection time will also become the research content of the next stage. |
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Zhang Xinan |
author_facet |
Zhang Xinan Li, Hongren |
format |
Theses and Dissertations |
author |
Li, Hongren |
author_sort |
Li, Hongren |
title |
Autonomous monitoring and fault detection for electrical motor driven belt system |
title_short |
Autonomous monitoring and fault detection for electrical motor driven belt system |
title_full |
Autonomous monitoring and fault detection for electrical motor driven belt system |
title_fullStr |
Autonomous monitoring and fault detection for electrical motor driven belt system |
title_full_unstemmed |
Autonomous monitoring and fault detection for electrical motor driven belt system |
title_sort |
autonomous monitoring and fault detection for electrical motor driven belt system |
publishDate |
2019 |
url |
http://hdl.handle.net/10356/78568 |
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1772828130818392064 |