一种用于旋转机械故障诊断的KurVMDPgram方法
By combining wavelet packet transform and Wiener filtering characteristics to construct the VMDPgram method, and using the kurtosis index to optimize the single parameter α, the multi-parameter optimization problem of VMD in rotating machinery fault diagnosis is solved, improving the stability of signal decomposition and the accuracy of fault feature extraction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUIZHOU UNIV
- Filing Date
- 2024-04-17
- Publication Date
- 2026-07-17
AI Technical Summary
Existing VMD methods for fault diagnosis of rotating machinery suffer from complex and difficult multi-parameter optimization, and the selection of the optimal intrinsic mode function is not accurate enough, resulting in low signal processing efficiency and insufficient accuracy.
Combining the binary decomposition structure of wavelet packet transform and the characteristics of Wiener filtering, a variational mode decomposition packet (VMDP) method is constructed. A kurtosis index is introduced to optimize a single parameter α. The optimal sub-component is selected by the cumulative kurtosis index, and the KurVMDPgram method is constructed to simplify the multi-parameter optimization problem.
The method realizes the transformation of VMD from multi-parameter optimization to single-parameter optimization, which improves the stability and accuracy of signal decomposition, enhances the sensitivity and computational efficiency of fault feature extraction, and can maintain high accuracy and reliability in complex environments.
Smart Images
Figure CN118132995B_ABST