| 引用本文: | 黄传金,宋海军,杨世锡,池永为,黄海舟,郝爽,郭胜彬.基于MVMD和全矢包络谱的滚动轴承故障诊断方法[J].电力自动化设备,2021,41(12): |
| HUANG Chuanjin,SONG Haijun,YANG Shixi,CHI Yongwei,HUANG Haizhou,HAO Shuang,GUO Shengbin.Fault diagnosis method of rolling bearing based on MVMD and full vector envelope spectrum[J].Electric Power Automation Equipment,2021,41(12): |
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| 摘要: |
| 为全面、准确地诊断滚动轴承故障，提出一种基于多元变分模态分解(MVMD)和全矢包络谱的滚动轴承故障诊断方法。首先，采用正交采样技术获取滚动轴承同一支撑处互相垂直方向上的振动信号，将其组成一个二元调制振荡信号。然后，运用MVMD从二元调制振荡信号中提取一组最佳的二元调制振荡信号，其对应的带宽之和最小。由于MVMD运用统一数学模型对2个方向的信号建模，可确保故障特征被分解到同一层，便于后续的信息融合。最后，运用Hilbert变换对每个二元调制振荡信号解调得到相应的包络信号，利用全矢谱融合2个方向的包络信号信息以得到全矢包络谱，进而诊断滚动轴承故障。仿真和试验结果证明了所提方法的可行性和有效性。 |
| 关键词: 多元变分模态分解 滚动轴承 故障诊断 全矢包络谱 |
| DOI：10.16081/j.epae.202107024 |
| 分类号:TK286.1 |
| 基金项目:国家自然科学基金资助项目(U1809219)；河南省高等学校重点科研项目(19A460029)；河南科技攻关项目(202102210077);2020年河南省大学创业创新项目(201011068010) |
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| Fault diagnosis method of rolling bearing based on MVMD and full vector envelope spectrum |
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HUANG Chuanjin1, SONG Haijun1, YANG Shixi2, CHI Yongwei2, HUANG Haizhou3, HAO Shuang1, GUO Shengbin1
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1.School of Mechanical and Electrical Engineering, Zhengzhou Institute of Technology, Zhengzhou 450044, China;2.School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China;3.Huadian Electric Power Research Institute Co.,Ltd.,Hangzhou 310030, China
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| Abstract: |
| In order to diagnose the rolling bearing faults comprehensively and accurately, a fault diagnosis method of rolling bearing based on MVMD(Multivariate Variational Mode Decomposition) and full vector envelope spectrum is proposed. Firstly, the quadrature sampling technology is used to obtain the vibration signals in the mutually perpendicular directions at the same support of the rolling bearing, which are formed into a binary modulation oscillation signal. Then, MVMD is used to extract a set of optimal binary modulation oscillation signals from the binary modulation oscillation signal, the sum of the corresponding bandwidth is the smallest. Because MVMD uses a unified mathematical model to build the signal models in two directions, it can ensure that the fault features are decomposed to the same layer to facilitate subsequent information fusion. Finally, the Hilbert transformation is used to demodulate each binary modulation oscillation signal to obtain the corresponding envelope signal. The envelope signal information in two directions is fused by full vector spectrum to obtain the full vector envelope spectrum to diagnose the rolling bearing fault. Simulative and test results prove the feasibility and effectiveness of the proposed method. |
| Key words: multivariate variational mode decomposition rolling bearing fault diagnosis full vector envelope spectrum |
