报告详情
Cyclic Band Sparsity Measures Driven Blind Deconvolution and its Application to Wind Turbine Bearing DiagnosisCyclic Band Sparsity Measures Driven Blind Deconvolution and its Application to Wind Turbine Bearing Diagnosis
编号：26
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更新：2025-06-15 09:07:12 浏览：35次
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摘要
Owing to the specialized operational conditions of wind turbine bearings, bearing fault feature is frequently distorted by environmental noise. Blind deconvolution methods are a validify method for machinery fault diagnosis, because it is significantly for reducing noise and eliminating the interference of the system transmission path. Among all the blind deconvolution methods, blind deconvolution based on traditional sparsity measures is an effective method for extracting weak periodic impulses related to bearing faults. However, in practical applications, a wind turbine may exist multiple cyclostationary sources and the measured signals may not be Gaussian distributed. Many conventional BD methods are influenced by non-fault cyclostationary component (CSC), leading to inaccurate identification of fault-related CSC. The performance of blind deconvolution based on sparsity measures can be reduced by the above problems. This study overcomes these challenges by proposing the novel indices, cyclic band sparsity measures, for blind deconvolution to extract the bearing fault more robustly. The proposed method, cyclic band sparsity measures (CBSM) driven blind deconvolution (CBSM-BD), can effectively to eliminate the influence of un-relate CSC interference and maximize the sparsity of the envelope spectrum at cyclic band. Examination of experimental results indicates that the proposed approach CBSM-BD is a straightforward yet effective method for enhancing faults exhibiting a period signature.
关键词
cyclostationary, Blind deconvolution, cyclic band sparsity measures
报告人
Jiahao Li
Doc Beijing University of Civil Engineering and Architecture稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月20日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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