2021
DOI: 10.1007/s10489-021-02504-1
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A semi-supervised transferable LSTM with feature evaluation for fault diagnosis of rotating machinery

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Cited by 23 publications
(10 citation statements)
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“…where h is the smoothing parameter and k : ð Þ is the Gaussian kernel function. Similarly, the probability density distribution function q x ð Þ of the sample point Y is obtained, and it is brought into formulas (11) and (12) to obtain the KLD value between a sample point and the center point in the same distribution. Some current studies have shown that due to the constant probability density function of the center point in the same distribution, KLD, which has better computational efficiency, is more suitable for the calculation of distribution differences than Wasserstein distance.…”
Section: Incremental Learning Networkmentioning
confidence: 99%
See 1 more Smart Citation
“…where h is the smoothing parameter and k : ð Þ is the Gaussian kernel function. Similarly, the probability density distribution function q x ð Þ of the sample point Y is obtained, and it is brought into formulas (11) and (12) to obtain the KLD value between a sample point and the center point in the same distribution. Some current studies have shown that due to the constant probability density function of the center point in the same distribution, KLD, which has better computational efficiency, is more suitable for the calculation of distribution differences than Wasserstein distance.…”
Section: Incremental Learning Networkmentioning
confidence: 99%
“…This mechanism enables the network to store and transmit information for a long time. The calculation formula of LSTM from input value to output value is given below 12,13 :…”
Section: Review Of Related Algorithmsmentioning
confidence: 99%
“…Better results can be obtained by extracting the time domain characteristics from the collected vibration signals and inputting them into the LSTM neural network for fault detection. To reflect the most comprehensive gear fault situation, this paper extracts 15 time-domain indexes based on the comprehensive consideration of literature [7] , which are mean value, standard deviation, absolute mean value, variance, root amplitude, maximum value, minimum value, root mean value, peak value, margin index, waveform index, pulse index, peak value, skewness and kurtosis. The detailed process of the fault detection model based on IPSO-LSTM proposed is shown in Figure1.…”
Section: Fault Detection Model Based On Ipso-lstmmentioning
confidence: 99%
“…At this time, 1) the loss generated by large-scale samples is large; 2) the loss generated by large-scale samples accounts for a large proportion. The researchers [23] proposed the amount function in the binary classification task, as shown in formula (6) (6) Where reduces the contribution of large-scale samples to the loss function. Compared with the cross-entropy loss, the loss of large-scale samples is reduced by , and the loss of effective samples is expanded, thereby highlighting the performance of small-scale sample accuracy.…”
Section: Focal Loss Functionmentioning
confidence: 99%