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Systems Science & Control Engineering

An Open Access Journal

Volume 8, 2020 - Issue 1

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Weak fault diagnosis of rolling bearing based on FRFT and DBN

Xing Hea School of Automation, Beijing Information Science and Technology University, Beijing, People’s Republic of ChinaView further author information

Jie Mab School of Mechatronics Engineering, Beijing Information Science and Technology University, Beijing, People’s Republic of ChinaCorrespondence[email protected]
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Pages 57-66 | Received 28 Oct 2019, Accepted 26 Jan 2020, Published online: 05 Feb 2020

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https://doi.org/10.1080/21642583.2020.1723143
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Citations (9)

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Articles from other publishers (9)

Yingqian Sun & Zhenzhen Jin. (2023) Research on fault diagnosis method of bearing based on parameter optimization VMD and improved DBN. Journal of Vibroengineering.
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Shaohua Qiu, Xiaopeng Cui, Zuowei Ping, Nanliang Shan, Zhong Li, Xianqiang Bao & Xinghua Xu. (2023) Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review. Sensors 23:3, pages 1305.
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Bingbing Hu, Jiahui Tang, Jimei Wu & Jiajuan Qing. (2022) An Attention EfficientNet-Based Strategy for Bearing Fault Diagnosis under Strong Noise. Sensors 22:17, pages 6570.
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J-h. Cai, Y-l. Xiao & L-y. Fu. (2021) Fault Diagnosis of Rolling Bearing Based on Fractional Fourier Instantaneous Spectrum. Experimental Techniques 46:2, pages 249-256.
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Jian Jiao & Xue-jiao Zheng. (2022) Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology. Computational Intelligence and Neuroscience 2022, pages 1-9.
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Jie Ma, Shule Li & Xinyu Wang. (2022) Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN. Symmetry 14:2, pages 320.
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Yongyi Chen, Dan Zhang & Wen-an Zhang. (2022) MSWR-LRCN: A new deep learning approach to remaining useful life estimation of bearings. Control Engineering Practice 118, pages 104969.
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Peng Zan, Rui Hong, Banghua Yang, Guofu Zhang, Yong Shao, Qiao Ding, Yutong Zhao & Hua Zhong. (2021) Diagnosis analysis of rectal function through using ensemble empirical mode decomposition–deep belief networks algorithm. Review of Scientific Instruments 92:6.
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Yang Dalian, Zhang Fanyu, Miao Jingjing, Zhang Hongxian, Li Renjie & Tao Jie. (2021) Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory. Mechanics & Industry 22, pages 24.
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