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• W4280555452 abstract "In the real application of chiller fault diagnosis models, the various working conditions may cause the different data distribution between test data and training data, which may lead to the lower diagnosis accuracy of models. In this paper, a novel fault diagnosis method for chillers across working conditions based on the domain knowledge-assisted Deep Extreme Learning Machine (DELM) is proposed. Firstly, the actual normal and performance degradation operation data of the chiller is collected through the Internet of Things Intelligent Agent (IOTIA). The Random Forest (RF) is used to determine the importance of features selected by domain knowledge analysis, and different feature subsets are selected as the model input. Then, the model of the DELM is used for fault diagnosis of chillers across varying temperature and load rate working conditions. Finally, experiments show that the proposed model achieves outstanding effect under the designed seven experimental across working conditions, indicating that the model has good generalization ability and is suitable for fault diagnosis across working conditions of chillers." @default.
• W4280555452 created "2022-05-22" @default.
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• W4280555452 date "2022-08-01" @default.
• W4280555452 modified "2023-10-16" @default.
• W4280555452 title "Across working conditions fault diagnosis for chillers based on IoT intelligent agent with deep learning model" @default.
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• W4280555452 doi "https://doi.org/10.1016/j.enbuild.2022.112188" @default.
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