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- W4296203710 abstract "Recently, graph data analysis receives attentions in mechanical fault diagnosis. Edge connection of the input graph indicates that neighbor nodes share the same fault type, but differences in importance between neighbor nodes is rarely demonstrated. Also, noise is unavoidable during signal acquisition, affecting the quality and reliability of constructed input graphs. In this paper, a robust rotating machinery diagnosis using a dynamic-weighted graph updating strategy is presented. Firstly, a non-weighted graph is constructed using the energy spectrum and the distance metric. Second, a linear weighting strategy is developed to assign weights to the edge connections, adjusting the influence of neighbor nodes quantitatively. Then, a dynamic edge-weight updating process is developed to decrease the negative influence of noisy samples using high-level output features during model training. Finally, node labels responding to the fault types of samples are identified. Case studies verified the effectiveness of the proposed method with noisy samples." @default.
- W4296203710 created "2022-09-18" @default.
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- W4296203710 date "2022-10-01" @default.
- W4296203710 modified "2023-10-15" @default.
- W4296203710 title "Robust rotating machinery diagnosis using a dynamic-weighted graph updating strategy" @default.
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- W4296203710 doi "https://doi.org/10.1016/j.measurement.2022.111895" @default.
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