2023
DOI: 10.1177/14759217231170723
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Incremental learning BiLSTM based on dynamic proportional adjustment mechanism and experience replay for quantitative detection of blade crack propagation

Abstract: In the traditional quantitative detection model for blade cracks in centrifugal fan, it is assumed that the data distribution is fixed or stable. However, the new data brought by the crack propagation would break the stable distribution, thereby disturbing the old data, and resulting in a decrease in the detection performance of the model. To overcome catastrophic forgetting and reduce the extra computational cost of retaining intact old data, a quantitative detection method based on incremental learning bidir… Show more

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Cited by 3 publications
(1 citation statement)
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“…Convolutional layers and max-pooling layers are used in all the convolution modules. The neuron activation function uses a ReLU to solve the network's gradient dispersion [20][21][22].…”
Section: Construction Of Feature Adaptation Extractormentioning
confidence: 99%
“…Convolutional layers and max-pooling layers are used in all the convolution modules. The neuron activation function uses a ReLU to solve the network's gradient dispersion [20][21][22].…”
Section: Construction Of Feature Adaptation Extractormentioning
confidence: 99%