AI Article Synopsis
- Traditional fault monitoring methods for sorting equipment mainly focus on the inlet and outlet, limiting insights into internal issues, which can compromise fault detection accuracy.
- A new fault monitoring method using a back propagation neural network has been developed specifically for the sorting device of domestic waste incineration slag, selecting key operational variables for better monitoring.
- Comparative experiments show that this neural network-based method significantly outperforms traditional monitoring techniques, highlighting its effectiveness and potential for real-world applications.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950583 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2024.e27396 | DOI Listing |
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