2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) 2022
DOI: 10.1109/ddcls55054.2022.9858533
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Fault Diagnosis of Rolling Bearing Based on Improved Convolutional Neural Network

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Cited by 3 publications
(5 citation statements)
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“…Then use the test set to test the network model to obtain the recognition accuracy of the model under each working condition. Then the one-dimensional convolutional neural network model 1DCNN (Li et al, 2020) and the adaptive one-dimensional convolutional neural network PSO-1DCNN are trained and tested in the same way, and the traditional method SVM (Shun et al, 2021) The accuracy of each model under variable working conditions.…”
Section: Analysis Of Experimental Resultsmentioning
confidence: 99%
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“…Then use the test set to test the network model to obtain the recognition accuracy of the model under each working condition. Then the one-dimensional convolutional neural network model 1DCNN (Li et al, 2020) and the adaptive one-dimensional convolutional neural network PSO-1DCNN are trained and tested in the same way, and the traditional method SVM (Shun et al, 2021) The accuracy of each model under variable working conditions.…”
Section: Analysis Of Experimental Resultsmentioning
confidence: 99%
“…And because the bearing generally plays the role of supporting the main shaft and transmitting the torque, it plays a decisive role in whether the equipment can work normally. Once the equipment fails, it may lead to catastrophic 10.3389/fnbot.2022.1044965 consequences, so it is particularly important for bearing fault diagnosis (Liu, 2017;Du et al, 2020;Li et al, 2020;Yu et al, 2020). When the bearing fails, the vibration signal generated has certain characteristics.…”
Section: Introductionmentioning
confidence: 99%
“…erefore, producers get a larger range of foraging. In each iteration, the location of the producer update principle is based on formula (10). i and j in formula (10) represent the position coordinates of the sparrow.…”
Section: Sparrow Search Algorithmmentioning
confidence: 99%
“…In each iteration, the location of the producer update principle is based on formula (10). i and j in formula (10) represent the position coordinates of the sparrow. e formula can be better understood according to the pseudocode in Table 1.…”
Section: Sparrow Search Algorithmmentioning
confidence: 99%
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