Fault monitoring method of domestic waste incineration slag sorting device based on back propagation neural network
© 2024 The Authors..
The main monitoring points of traditional sorting equipment fault monitoring methods are usually limited to the inlet and outlet, making it difficult to monitor the internal equipment, which may affect the accuracy of fault monitoring. Therefore, a new fault monitoring method based on back propagation neural network has been studied and designed, which is mainly applied to the sorting device of domestic waste incineration slag. The fault monitoring modeling variables of the domestic waste incineration slag sorting device are selected to determine the operation status of the sorting device. Based on back propagation neural network, a fault monitoring model for the sorting device of municipal solid waste incinerator slag is constructed, and the fault data of the sorting device is trained in the model, so that the fault data of the sorting device can be optimized faster, thus improving the accuracy of fault monitoring. Through comparative experiments with traditional methods, it has been confirmed that this fault monitoring method based on back propagation neural network has significant advantages in detection performance, demonstrating its potential in practical applications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 6 vom: 30. März, Seite e27396 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xu, Hao [VerfasserIn] |
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| Links: |
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| Themen: |
Accuracy |
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| Anmerkungen: |
Date Revised 22.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e27396 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM369996151 |
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| 520 | |a © 2024 The Authors. | ||
| 520 | |a The main monitoring points of traditional sorting equipment fault monitoring methods are usually limited to the inlet and outlet, making it difficult to monitor the internal equipment, which may affect the accuracy of fault monitoring. Therefore, a new fault monitoring method based on back propagation neural network has been studied and designed, which is mainly applied to the sorting device of domestic waste incineration slag. The fault monitoring modeling variables of the domestic waste incineration slag sorting device are selected to determine the operation status of the sorting device. Based on back propagation neural network, a fault monitoring model for the sorting device of municipal solid waste incinerator slag is constructed, and the fault data of the sorting device is trained in the model, so that the fault data of the sorting device can be optimized faster, thus improving the accuracy of fault monitoring. Through comparative experiments with traditional methods, it has been confirmed that this fault monitoring method based on back propagation neural network has significant advantages in detection performance, demonstrating its potential in practical applications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Accuracy | |
| 650 | 4 | |a Back propagation neural network | |
| 650 | 4 | |a Domestic waste | |
| 650 | 4 | |a Fault diagnosis | |
| 650 | 4 | |a Monitoring model | |
| 650 | 4 | |a Sorting device | |
| 700 | 1 | |a Huan, Dongdong |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Jihong |e verfasserin |4 aut | |
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