Gearbox fault diagnosis method based on convergent trend-guided variational mode decomposition
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摘要: 从中心频率的角度出发，深入分析变分模式分解算法中不同初始中心频率的分解特性；利用分解特性对变分模式分解中使用的初始中心频率进行合理更新，在没有先验知识的情况下自适应分解信号的整个分析频带；根据峭度准则，从分解的子信号中选取包含故障信息最丰富的故障分量；对选出的最佳故障分量进行平衡参数优化和稀疏编码收缩处理，并进行包络分析；基于变分模式分解的特性，构建一套完整的基于收敛趋势变分模式分解的齿轮箱故障诊断方法，并应用诊断方法于汽车变速器齿轮箱中齿轮早期局部损伤故障识别和齿轮接触疲劳试验机中齿轮箱故障诊断。研究结果表明：在变分模式分解算法中存在着收敛趋势现象，随着初始中心频率的逐渐增大，所提取模式的收敛中心频率与其相对应的初始中心频率具有特定的收敛关系；提出的方法无需参数先验知识，可自适应地将振动信号进行分解；试验1中提出的方法分解得到的故障分量峭度为3.056，优化处理后故障分量的峭度为24.812，传统的2种初始化中心频率变分模式分解方法的故障分量最大峭度分别为2.830和2.421，快速谱峭度分析方法未能提取出故障分量；试验2中诊断方法分解得到的故障分量峭度为3.467，优化处理后故障分量的峭度为19.780，传统的2种初始化中心频率变分模式分解方法的故障分量最大峭度分别为3.231和3.361，快速谱峭度分析方法未能提取出故障分量；提出的方法能够增强瞬态特征和故障特征频率，在齿轮箱故障诊断方面更具准确性和优越性。Abstract: From the perspective of the center frequency, the decomposition characteristics of different initial center frequencies in the variational mode decomposition algorithm were deeply analyzed. Making use of the decomposition characteristics, the initial center frequencies used in the variational mode decomposition were reasonably updated, without the prior knowledge, the entire analysis frequency band of the signal was adaptively decomposed. According to the kurtosis criterion, the fault component with the most abundant fault information was selected from the decomposed sub-signals. Envelope analysis was performed on the optimal fault component which has been processed by the balance parameter optimization and sparse code shrinkage. Based on the decomposition characteristics of variational mode, a complete gearbox fault diagnosis method was constructed based on the convergent trend-guided variational mode decomposition, and the diagnosis method was applied to the early local damage fault identification of gears in automobile transmission gearboxes and fault diagnosis of gearboxes in contact fatigue testing machines. Research results show that there is a convergent trend phenomenon in the variational mode decomposition algorithm. With the gradual increase of the initial center frequency, the convergent center frequency of the extracted mode has a specific convergent relationship with its corresponding initial center frequency. The proposed method can decompose the vibration signal adaptively without the prior knowledge of parameters. In experiment 1, the kurtosis of the fault component obtained by the proposed method is 3.056, and the kurtosis of the fault component after optimization is 24.812. The maximum kurtosis of the fault component in the traditional variational mode decomposition with two different ways for initializing the center frequency is 2.830 and 2.421, respectively. The fast spectral kurtosis analysis method fails to extract the fault component. In experiment 2, the kurtosis of fault component obtained by the proposed method is 3.467, and the kurtosis of the fault component after optimization is 19.780. The maximum kurtosis of the fault component in the traditional variational mode decomposition with two different ways for initializing the center frequency is 3.231 and 3.361, respectively. The fast spectral kurtosis analysis method fails to extract the fault components. The proposed method can enhance the transient characteristics and fault characteristic frequencies, and is more accurate and superior in the gearbox fault diagnosis. 22 figs, 30 refs.
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图 5 基于收敛趋势VMD的齿轮箱故障诊断方法的流程
Figure 5. Flow of gearbox fault diagnosis method based on convergent trend-guided VMD
图 10 Kurtosis of 4 modes extracted by proposed method
Figure 10. Analysis results of optimal mode M2 obtained by proposed method
图 13 零初始化ICF的传统VMD提取的M3、M4的包络谱
Figure 13. Envelope spectra of M3 and M4 extracted by conventional VMD with zero initialization of ICF
图 18 本文方法所得最优模式M1的分析结果
Figure 18. Results of optimal mode M1 analyzed by proposed method
图 19 图 15(a)试验信号的快速谱峭度分析结果
Figure 19. Results of experimental signal in Fig. 15(a) analyzed by fast spectral kurtosis analysis
图 20 采用不同初始化ICF方式的传统VMD对图 15(a)试验信号的分析结果
Figure 20. Results of experimental signal in Fig. 15(a) analyzed by conventional VMD with different initialization ways of ICF
图 21 均匀间隔分布中心频率的传统VMD提取的M1、M2的包络谱
Figure 21. Envelope spectra of M1 and M2 extracted by conventional VMD with uniformly spaced distribution center frequency
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