基于ACYCBD的风电机组轴承故障诊断与失效分析  

Fault Diagnosis and Failure Analysis of Wind Turbine Bearing Based on ACYCBD

作　　者：汪韵朗 王子顺 WANG Yunlang;WANG Zishun(Mingyang Smart Energy Group Co.,Ltd.,Zhongshan 528400,China)

机构地区：[1]明阳智慧能源集团股份公司,中山528400

出　　处：《微特电机》2025年第2期79-85,共7页Small & Special Electrical Machines

摘　　要：滚动轴承是机械旋转系统的核心元件,其稳定运行对确保系统的安全和经济性至关重要。通过振动故障诊断对轴承故障的识别与定位,通过失效分析可以深入了解轴承故障的根本原因,为制定有效的维修策略和预防措施提供依据。针对某风电机组轴承内圈开裂故障,采用自适应最大二阶循环平稳盲反卷积(ACYCBD)方法提取轴承振动信号中的循环平稳特征,进行故障特征的提取识别和定位。采用失效分析揭示了裂纹源处的白色组织导致了轴承的开裂。研究结果表明,ACYCBD方法在轴承故障诊断中具有良好的可行性和有效性,并结合失效分析可以全面揭示故障原因,显著提高故障诊断的准确性和效率,保障机械设备的稳定运行,具有重要的实际意义。Rolling bearings are the core components of rotating mechanical systems and their stable operation is critical to the safety and economy of the system.The identification and localization of bearing faults through vibration fault diagnosis and failure analysis can provide insight into the root causes of bearing failures and a basis for developing effective maintenance strategies and preventive measures.For a wind turbine bearing inner ring cracking fault,the adaptive maximum second-order cyclostationarity blind deconvolution(ACYCBD)method was used to extract the cyclic smooth features in the bearing vibration signal,and the fault feature extraction is used for identification and localization.Failure analysis was used to reveal that the white tissue at the crack source caused the bearing to crack.The results show that the ACYCBD method had good feasibility and effectiveness in bearing fault diagnosis,and combined with failure analysis,can comprehensively reveal the cause of the fault,greatly improve the accuracy and efficiency of fault diagnosis,and ensure the stable operation of mechanical equipment,which is of great practical significance.

关 键 词：自适应最大二阶循环平稳盲反卷积 轴承故障诊断 振动信号分析 失效分析 

分 类 号：TM315[电气工程—电机]