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An Improved Wind Turbine Bearing Fault Diagnosis Method Based on POSGMD and ICNN Under Strong Noise Scenarios

Weizhong Zhang Xiaoan Yan Maoyou Ye Xing Hua Dong Jiang

Journal of Harbin Institute of Technology(New Series)2026，Vol.33Issue(1)：P.1-19,19.

Journal of Harbin Institute of Technology(New Series)2026，Vol.33Issue(1)：P.1-19,19.DOI:10.11916/j.issn.1005-9113.2024102

An Improved Wind Turbine Bearing Fault Diagnosis Method Based on POSGMD and ICNN Under Strong Noise Scenarios

Weizhong Zhang ¹Xiaoan Yan ¹Maoyou Ye ¹Xing Hua ¹Dong Jiang¹

作者信息

1. School of Mechatronics Engineering,Nanjing Forestry University,Nanjing 210037,China
折叠

摘要

关键词

symplectic geometry mode decomposition/convolutional neural network/deep learning/rolling bearing/fault diagnosis/anti⁃noise robustness

分类

信息技术与安全科学

引用本文复制引用

Weizhong Zhang,Xiaoan Yan,Maoyou Ye,Xing Hua,Dong Jiang..An Improved Wind Turbine Bearing Fault Diagnosis Method Based on POSGMD and ICNN Under Strong Noise Scenarios[J].Journal of Harbin Institute of Technology(New Series),2026,33(1):P.1-19,19.

基金项目

Jiangsu Association for Science and Technology Youth Talent Support Project(Grant No.JSTJ-2024-031) （Grant No.JSTJ-2024-031）

National Natural Science Foundation of China(Grant No.52005265) （Grant No.52005265）

Natural Science Fund for Colleges and Universities in Jiangsu Province(Grant No.20KJB460002)。（Grant No.20KJB460002）

Journal of Harbin Institute of Technology(New Series)

ISSN：1005-9113

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