Details

Autor(en) / Beteiligte

• Deng, Cong
• Li, Xu-Feng
• Ji, Peng
• Li, Jing-Sheng
• Hou, Qun

Titel

Research on fault diagnosis of gas steam boilers based on deep neural networks

Ist Teil von

• Journal of physics. Conference series, 2022-11, Vol.2366 (1), p.12034

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Abstract Gas steam boilers are special equipment subjected to high temperature and pressure, it is particularly important to detect and deal with operation faults in time. Against the 4 types of common fault of gas steam boilers, the fault diagnosis model was established based on deep neural networks (DNN), and the model parameters were optimized and verified by experiments. The results show that the fault diagnosis model performs the best as Tanh of the activation function, 100 of Batch_Size, Adam of Optimizer, 10 -5 of learning rate and 0.2 of Dropout since the output accuracy is above 92% and the model begins to converge with iterative times of 12. The accuracy of fault identification of reaches 100% in verification experiments and the model can still effectively recognize the dataset from the early stage which suggests that the fault diagnosis based on deep neural networks has high accuracy and provides a reliable solution for the safety supervision of special equipments.