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

Abstract: 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.

Key words: adaptive maximum second-order cyclostationarity blind deconvolution ( ACYCBD ), bearing fault diagnosis, vibration signal analysis, failure analysis