Miao Yonghao, Zhao Ming, Yi Yinggang, Lin Jing
School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China.
School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China; School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
ISA Trans. 2020 Apr;99:496-504. doi: 10.1016/j.isatra.2019.10.005. Epub 2019 Oct 11.
Encoder signal as the built-in information is always used for the speed and motion control. Meanwhile, it has remarkable superiority in the fault diagnosis of gearbox compared with the popular vibration signal. Traditional decomposition method, such as EMD, gradually loses competitiveness with the increase of the complexity of the encoder signal. To solve the problem, with aid of the unique characteristic of encoder signal and the decomposition performance of variational mode decomposition (VMD), a new sparsity-oriented VMD (SOVMD), is originally designed and initially introduced for encoder signal analysis in this paper. Firstly, SOVMD is free from the selection of mode number and initial center frequency (ICF), which troubles seriously the application of VMD. Since a prior ICF which coarsely indicates the location of the fault band can enhance the decomposing efficiency of VMD, ICF = 0 is more appropriate and easier for the extraction of fault information concentrated in the low frequency region. Benefiting from the characteristics of distribution, the optimization of the mode number is unnecessary since the fault mode will generate in the first mode. Secondly, with the proposed selection criterion of the balance parameter, SOVMD can decompose the mode with most fault information more effectively and accurately. Furthermore, a sparsity operation which is originally designed for the encoder signal analysis can further suppress noise and enhance the fault impulses. Through the simulation and experimental cases from the planet gearbox bench, the feasibility and effectiveness of SOVMD can be verified. Therefore, it is reasonable to conclude that the proposed SOVMD is an alternative scheme for gearbox fault diagnosis based on built-in encoder information.
作为内置信息的编码器信号一直用于速度和运动控制。同时，与常用的振动信号相比，它在齿轮箱故障诊断中具有显著优势。传统的分解方法，如经验模态分解（EMD），随着编码器信号复杂度的增加逐渐失去竞争力。为解决该问题，借助编码器信号的独特特性和变分模态分解（VMD）的分解性能，本文首次设计并引入了一种新的面向稀疏性的VMD（SOVMD）用于编码器信号分析。首先，SOVMD无需选择模态数和初始中心频率（ICF），而这严重困扰着VMD的应用。由于一个大致指示故障频段位置的先验ICF可以提高VMD的分解效率，ICF = 0对于提取集中在低频区域的故障信息更合适且更容易。受益于分布特性，由于故障模态会在第一模态中产生，因此无需对模态数进行优化。其次，通过所提出的平衡参数选择准则，SOVMD可以更有效、准确地分解包含最多故障信息的模态。此外，专门为编码器信号分析设计的稀疏性运算可以进一步抑制噪声并增强故障脉冲。通过行星齿轮箱试验台的仿真和实验案例，可以验证SOVMD的可行性和有效性。因此，可以合理地得出结论，所提出的SOVMD是基于内置编码器信息进行齿轮箱故障诊断的一种替代方案。
