Xu Hao, Huan Dongdong, Lin Jihong
School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215137, China.
School of Ecological Environment and Urban Construction, Fujian University of Technology, Fuzhou, 350118, China.
Heliyon. 2024 Mar 4;10(6):e27396. doi: 10.1016/j.heliyon.2024.e27396. eCollection 2024 Mar 30.
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.
传统分拣设备故障监测方法的主要监测点通常局限于进出口，难以对设备内部进行监测，这可能会影响故障监测的准确性。因此，研究并设计了一种基于反向传播神经网络的新型故障监测方法，该方法主要应用于生活垃圾焚烧炉渣的分拣装置。选取生活垃圾焚烧炉渣分拣装置的故障监测建模变量，以确定分拣装置的运行状态。基于反向传播神经网络，构建了城市生活垃圾焚烧炉渣分拣装置的故障监测模型，并在该模型中对分拣装置的故障数据进行训练，从而使分拣装置的故障数据能够更快地得到优化，进而提高故障监测的准确性。通过与传统方法的对比实验，证实了这种基于反向传播神经网络的故障监测方法在检测性能方面具有显著优势，展现了其在实际应用中的潜力。
