摘要
针对强背景噪声下行星齿轮箱早期微弱故障特征难以被识别且变分模态分解算法中模态个数K和惩罚因子α需要依赖人为经验反复尝试而不能自适应确定的问题,提出了改进变分模态分解(Modified Variational Mode Decomposition,MVMD)方法,通过故障信号尺度空间谱的自适应分割来确定所需模态个数K,同时建立峭度最大值判定准则自动选取最佳惩罚因子α。在此基础上,将MVMD与自适应非凸重叠组收缩降噪算法(Adaptive Nonconvex Overlap Group Shrinkage,ANOGS)相融合,提出了基于MVMDANOGS的行星齿轮箱早期故障诊断方法。对故障振动信号进行MVMD最佳分解,获取多个模态分量;利用ANOGS算法对峭度最大的敏感模态进行稀疏降噪,从而突出信号中的故障冲击特征;对降噪模态进行包络解调处理,以提取明显的故障特征频率来判别故障。通过仿真信号和工程实验数据分析表明,相比传统VMD方法、EEMD方法和快速谱峭度方法,该方法能成功地提取微弱故障冲击特征且更加清晰,提高了行星齿轮箱早期故障的表征能力与诊断精度。
Aiming at the problems that the incipient weak fault characteristics of the planetary gearbox are difficult to be identified under strong background noise,and the mode number K and penalty factorαof variational mode decomposition(VMD)must be set in advance and cannot be adaptively determined,a modified variational mode decomposition(MVMD)algorithm is proposed.The MVMD method determines the mode number K by adaptive scale space spectrum segmentation of the fault signal,and establishes the maximum kurtosis criterion to automatically select the penalty factorα.On this basis,a novel method for incipient fault diagnosis of planetary gearbox based on the MVMD and adaptive non-convex overlap group shrinkage(MVMD-ANOGS)is proposed.The collected vibration signal of the planetary gearbox is decomposed by MVMD,and then a series of narrow-band modal components are obtained.The proposed ANOGS algorithm performs sparse denoising on the sensitive mode with the maximum kurtosis,thereby highlighting the fault impact characteristics of the mode signal.The envelope demodulation analysis is performed on the denoised mode,and the obvious fault characteristic frequencies are extracted from its envelope spectrum.The feasibility of the proposed method is validated using both the numerical simulation and practical experimental dates of planetary gearbox.Moreover,compared with the traditional VMD,EEMD and fast spectral kurtosis methods,the proposed method can extract clearly weak fault impact features and improve the incipient fault identification accuracy of planetary gearbox.
作者
王朝阁
李宏坤
曹顺心
周强
刘艾强
任学平
WANG Chao-ge;LI Hong-kun;CAO Shun-xin;ZHOU Qiang;LIU Ai-qiang;REN Xue-ping(School of Logistics Engineering,Shanghai Maritime University,Shanghai 201306,China;School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China;Institute of Mechanical Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2021年第6期1293-1304,共12页
Journal of Vibration Engineering
基金
国家重点研发计划项目(2019YFB2004600)。
关键词
故障诊断
行星齿轮箱
变分模态分解
重叠组收缩算法
特征提取
fault diagnosis
planetary gearbox
variational mode decomposition
overlap group shrinkage algorithm
feature extraction
