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• W4365451950 endingPage "147592172311637" @default.
• W4365451950 startingPage "147592172311637" @default.
• W4365451950 abstract "Accurate diagnosis of the damage location of the frame structure is very important for the overall damage assessment and subsequent maintenance of the frame structure. However, the frame structure generally works in the noise environment, which increases the difficulty of health monitoring and fault diagnosis of frame structure based on vibration data. In order to realize the accurate damage location diagnosis of structural frame under noise environment, this paper proposes a fault diagnosis method based on wavelet denoising, convolutional neural network, Inception module, and long short-term memory (LSTM) on the basis of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In order to verify the effectiveness and superiority of the method proposed in this paper, the 4-story steel frame model of the University of British Columbia is taken as the research object, and the experiments are carried out with the method proposed in this paper, and under the same conditions, the comparative experiments are carried out with other similar methods. The experimental results show that the method proposed in this paper not only has high accuracy, but also has strong anti-noise ability, and its performance is better than other similar methods. Therefore, the fault diagnosis method proposed in this paper can effectively perform the accurate diagnosis of damage location of frame structure under noise environment." @default.
• W4365451950 created "2023-04-15" @default.
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• W4365451950 date "2023-04-13" @default.
• W4365451950 modified "2023-09-30" @default.
• W4365451950 title "Damage location diagnosis of frame structure based on wavelet denoising and convolution neural network implanted with Inception module and LSTM" @default.
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• W4365451950 doi "https://doi.org/10.1177/14759217231163777" @default.
• W4365451950 hasPublicationYear "2023" @default.
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