Rolling Bearing Fault Diagnosis Method Based on Attention CNN and BiLSTM Network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s11063-022-11013-2.pdf
Reference47 articles.
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2. Huang W, Sun H, Luo J, Wang W (2019) Periodic feature oriented adapted dictionary free OMP for rolling element bearing incipient fault diagnosis. Mech Syst Signal Process 126:137–160. https://doi.org/10.1016/j.ymssp.2019.02.023
3. Ribeiro Junior RF, Areias IAdS, Gomes GF (2021) Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types. Sens Rev 41(3):311–319. https://doi.org/10.1108/SR-02-2021-0052
4. Betta G, Liguori C, Paolillo A, Pietrosanto A (2002) A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis. IEEE Trans Instrum Meas 51(6):1316–1322. https://doi.org/10.1109/TIM.2002.807987
5. Wen L, Gao L, Li X, Xie M, Li G (2017) A new data-driven intelligent fault diagnosis by using convolutional neural network. In: 2017 IEEE international conference on industrial engineering and engineering management (IEEM), pp 813–817. https://doi.org/10.1109/IEEM.2017.8290004
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