1.西南交通大学 机械工程学院,四川 成都 610031
2.中车唐山机车车辆有限公司,河北 唐山 063035
朱丹(1995—),女,硕士研究生,研究方向为信号分析与故障诊断。
扫 描 看 全 文
朱丹, 苏燕辰, 燕春光. 基于SVD-MOMEDA的高速列车齿轮箱轴承故障诊断[J]. 机车电传动, 2020,(2):144-148,152.
Dan ZHU, Yanchen SU, Chunguang YAN. Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA[J]. Electric Drive for Locomotives, 2020,(2):144-148,152.
朱丹, 苏燕辰, 燕春光. 基于SVD-MOMEDA的高速列车齿轮箱轴承故障诊断[J]. 机车电传动, 2020,(2):144-148,152. DOI: 10.13890/j.issn.1000-128x.2020.02.126.
Dan ZHU, Yanchen SU, Chunguang YAN. Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA[J]. Electric Drive for Locomotives, 2020,(2):144-148,152. DOI: 10.13890/j.issn.1000-128x.2020.02.126.
针对强背景噪声环境下高速列车齿轮箱轴承故障信号难以检测的问题以及多点优化最小熵解卷积修正(multipoint optimal minimum entropy deconvolution adjusted,MOMEDA)方法受滤波器阶数、故障周期影响的问题,提出了基于奇异值分解(singular value decomposition, SVD)改进的MOMEDA的轴承故障诊断方法。首先采用SVD作为MOMEDA的前置滤波器滤除部分噪声,然后通过MOMEDA多点峭度谱追踪故障周期成分,采用变步长搜索法迭代求解MOMEDA滤波器最优阶数,最后利用最优参数相对应的MOMEDA增强信号中的周期性脉冲,并通过包络谱提取故障特征。仿真信号和试验数据分析表明:该方法能实现高速列车齿轮箱轴承故障的精确诊断,且故障诊断效果优于互补经验模态分解方法。
Aiming at problems of high-speed train gearbox bearing fault signals being difficult to detect under strong noise background, and the problem that the multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) method was affected by the order of filter and the period of impulse signal, an improved MOMEDA method for bearing fault diagnosis based on singular value decomposition(SVD) was proposed. Firstly, SVD was used as the pre-filter of MOMEDA to filter the partial noise. Then, the fault period component was traced by MOMEDA multipoint kurtosis spectrum, and the optimal order of MOMEDA filter was solved iteratively by variable step search method. Finally, by using the periodic impulse in the signal track with MOMEDA, and the fault features with envelope spectrum were extracted. The simulation signal and the fault test data showed that this method could accurately diagnose the fault of the gearbox bearing of high-speed train, and the fault diagnosis effect was better than the complementary empirical mode decomposition method.
高速列车故障诊断多点优化最小熵解卷积修正奇异值分解滚动轴承齿轮箱
high-speed trainfault diagnosisMOMEDASVDrolling bearinggearbox
杨琦, 陈智才. 基于EMD和相关系数法的列车滚动轴承故障诊断方法研究[J]. 电力机车与城轨车辆, 2018, 41(3): 15-17.
张菀, 贾民平, 朱林. 一种自适应Morlet小波滤波方法及其在滚动轴承早期故障特征提取中的应用[J]. 东南大学学报(自然科学版), 2016, 46(3): 457-463.
LI Y B, XU M Q, HUANG W H, et al. An improved EMD method for fault diagnosis of rolling bearing[C]//IEEE. 2016 Prognostics and System Health Management Conference. Chengdu: IEEE, 2016: 1-5. DOI: 10.1109/PHM.2016.7819842http://doi.org/10.1109/PHM.2016.7819842.
陈海周, 王家序, 汤宝平,等. 基于最小熵解卷积和Teager能量算子直升机滚动轴承复合故障诊断研究[J]. 振动与冲击, 2017, 36(9): 45-50.
WIGGINS R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16(1/2): 21-35.
SAWALHI N, RANDALL R B, ENDO H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J]. Mechanical Systems and Signal Processing, 2007, 21(6): 2616-2633.
MCDONALD G L, ZHAO Q, ZUO M J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing, 2012, 33(1): 237-255.
MCDONALD G L, ZHAO Q. Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection[J]. Mechanical Systems and Signal Processing, 2017, 82: 461-477.
祝小彦, 王永杰. 基于MOMEDA与Teager能量算子的滚动轴承故障诊断[J]. 振动与冲击, 2018, 37(6): 104-110.
赵磊, 张永祥, 朱丹宸. 基于MOMEDA和IITD的滚动轴承微弱故障特征提取[J]. 海军工程大学学报, 2019, 31(1): 57-61.
王志坚, 张纪平, 王俊元,等. 基于MED-MOMEDA的风电齿轮箱复合故障特征提取研究[J]. 电机与控制学报, 2018, 22(9): 111-118.
秦毅, 张清亮, 赵月. 基于自适应奇异值分解的行星齿轮箱故障诊断方法[J]. 振动与冲击, 2018, 37(17): 122-127.
龙莹, 苏燕辰, 李艳萍,等. EWT-SVD在高速列车万向轴动不平衡检测中的应用[J]. 中国测试, 2018, 44(5): 24-30.
赵学智, 叶邦彦, 陈统坚. 奇异值差分谱理论及其在车床主轴箱故障诊断中的应用[J]. 机械工程学报, 2010, 46(1): 100-108.
王建国, 李健, 刘颖源. 一种确定奇异值分解降噪有效秩阶次的改进方法[J]. 振动与冲击, 2014, 33(12): 176-180.
王友仁, 陈伟, 孙灿飞,等. 基于能量聚集度经验小波变换的齿轮箱早期微弱故障诊断[J]. 中国机械工程, 2017, 28(12): 1484-1490.
0
浏览量
7
下载量
0
CSCD
0
CNKI被引量
关联资源
相关文章
相关作者
相关机构