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Original Articles

Application of Cyclic Spectral Analysis in Diagnosis of Bearing Faults in Complex Machinery

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Pages 1151-1158 | Received 10 Mar 2013, Accepted 07 May 2015, Published online: 01 Sep 2015

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Lingli Cui, Xinyuan Zhao, Dongdong Liu & Huaqing Wang. (2023) A novel spectral coherence-based weighted envelope spectrum analysis method for bearing fault diagnosis. Structural Health Monitoring.
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Shengnan Tang, Shouqi Yuan & Yong Zhu. (2020) Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery. Processes 8:10, pages 1217.
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Hongchao Wang & Wenliao Du. (2020) Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery. Complexity 2020, pages 1-14.
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Jimeng Li, Qingwen Yu, Xiangdong Wang & Yungang Zhang. (2020) An enhanced rolling bearing fault detection method combining sparse code shrinkage denoising with fast spectral correlation. ISA Transactions 102, pages 335-346.
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Hongchao Wang & Wenliao Du. (2020) An improved spectrum correlation time-frequency analysis method and its application in fault diagnosis of rolling element bearing. Journal of Vibroengineering 22:4, pages 792-803.
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Anil Kumar & Rajesh Kumar. (2018) Role of Signal Processing, Modeling and Decision Making in the Diagnosis of Rolling Element Bearing Defect: A Review. Journal of Nondestructive Evaluation 38:1.
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Yongwei Chi, Shixi Yang, Weidong Jiao, Jun He, Xiwen Gu & Evangelos Papatheou. (2019) Spectral DCS-based feature extraction method for rolling element bearing pseudo-fault in rotor-bearing system. Measurement 132, pages 22-34.
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