轴承振动信号的处理方法和轴承振动信号的处理系统
By combining EEMD and K-SVD, the noise reduction problem in the early stage of bearing failure was solved, and more efficient fault feature extraction and diagnosis were achieved.
CN116296395BActive Publication Date: 2026-07-17SHENHUA SHENDONG COAL GRP +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENHUA SHENDONG COAL GRP
- Filing Date
- 2023-03-15
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
In existing technologies, the K-SVD algorithm has poor noise reduction performance in the early stages of bearing failure, making bearing fault diagnosis difficult.
Method used
The EEMD algorithm is used to reconstruct the signal to be measured, and after noise reduction, the K-SVD algorithm is used for dictionary learning. The dictionary learning model is optimized by combining TQWT and OMP algorithms to improve noise resistance.
Benefits of technology
It improves the noise reduction effect in the early stage of bearing failure, ensures the accurate extraction of fault feature information, and supports early fault diagnosis.
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Abstract
本申请提供了一种轴承振动信号的处理方法和轴承振动信号的处理系统。该方法包括：从振动信号中选取一个作为待测量信号，待测量信号表征轴承已发生故障且为初始等级振动信号中的一个；采用EEMD算法对待测量信号进行重构，得到重构信号；比较待测量信号的第一信噪比与重构信号的第二信噪比，在第一信噪比小于第二信噪比的情况下，确定振动信号中的噪声已削弱。本方案将轴承在早期出现故障的待测量信号选取出来，采用EEMD算法来对待测量信号进行去噪处理，可以使得K‑SVD的算法的抗噪能力提高，保证了本方案的去噪效果较好。
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