Bearing fault diagnosis base on multi-scale CNN and LSTM model
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DOI: 10.1007/s10845-020-01600-2
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References listed on IDEAS
- Chenxi Wu & Tefang Chen & Rong Jiang & Liwei Ning & Zheng Jiang, 2017. "A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1847-1858, December.
- Adrián Rodríguez Ramos & José M. Bernal de Lázaro & Alberto Prieto-Moreno & Antônio José Silva Neto & Orestes Llanes-Santiago, 2019. "An approach to robust fault diagnosis in mechanical systems using computational intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1601-1615, April.
- Cong Wang & Meng Gan & Chang’an Zhu, 2018. "Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 937-951, April.
Citations
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Cited by:
- Su, Yunsheng & Shi, Luojie & Zhou, Kai & Bai, Guangxing & Wang, Zequn, 2024. "Knowledge-informed deep networks for robust fault diagnosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
- Sangho Lee & Jeongsub Choi & Youngdoo Son, 2024. "Efficient visibility algorithm for high-frequency time-series: application to fault diagnosis with graph convolutional network," Annals of Operations Research, Springer, vol. 339(1), pages 813-833, August.
- Cuixia Jiang & Hao Chen & Qifa Xu & Xiangxiang Wang, 2023. "Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1667-1681, April.
- Chuanxia Jian & Yinhui Ao, 2023. "Imbalanced fault diagnosis based on semi-supervised ensemble learning," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3143-3158, October.
- Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.
- Li, Xin & Li, Shuhua & Wei, Dong & Si, Lei & Yu, Kun & Yan, Ke, 2024. "Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
- Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
- Huanjie Wang & Yuan Li & Xiwei Bai & Jingwei Li & Jie Tan & Chengbao Liu, 2024. "Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3131-3148, October.
- Roman Rodriguez-Aguilar & Jose-Antonio Marmolejo-Saucedo & Utku Köse, 2024. "Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators," Mathematics, MDPI, vol. 12(19), pages 1-17, October.
- Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
- Hanting Zhou & Wenhe Chen & Jing Liu & Longsheng Cheng & Min Xia, 2024. "Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3523-3542, October.
- Jiayang Liu & Fuqi Xie & Qiang Zhang & Qiucheng Lyu & Xiaosun Wang & Shijing Wu, 2024. "A multisensory time-frequency features fusion method for rotating machinery fault diagnosis under nonstationary case," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3197-3217, October.
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Keywords
Bearing fault diagnosis; Convolutional neural network; Recurrent neural network; Deep learning; Feature fusion;All these keywords.
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