|
|
Fault Diagnosis Method of Rolling Bearing Based on Improved Variational Mode Decomposition |
MENG Zong,Lü Meng,YIN Na,LI Jing |
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province,Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
Abstract A fault diagnosis method for bearing fault signal with improved variational mode decomposition was proposed. Firstly, the improved singular value decomposition denoising method was used to denoise the signal, and then the signal was subjected to variational mode decomposition.By using the ratio of the sum of the energy of the component signal to the original signal energy, the decomposition effect of the variational mode decomposition was judged to find the optimal decomposition layer. And based on the correlation coefficient between the component signals, it was judged whether the component signal adjacent to the center frequency was from the same modulation portion in the signal. Finally, the fault characteristic frequency was found by the envelope spectrum of the main component, and the fault type was judged.The weak frequency characteristic information was successfully extracted by processing the simulated signal and the actual bearing fault signal, and the effectiveness of the method was verified.
|
Received: 11 September 2018
Published: 08 June 2020
|
|
Corresponding Authors:
Zong MENG
E-mail: mzysu@ysu.edu.cn
|
|
|
|
[1]Sugumaran V. Ramachandran K I. Effect of number features on classification of roller bearing faults using SVM and PSVM[J]. Expert Systems with Applications, 2011, 38(4): 4088-4096.
[2]An X L, Jiang D X, Chen J, et al. Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing[J]. Journal of Vibration and Control, 2012, 18(2): 240-245.
[3]孟宗, 赵东方, 李晶, 等. 基于局部均值分解多尺度模糊熵和灰色相似关联度的滚动轴承故障诊断[J]. 计量学报, 2018, 39(2): 231-236.
Meng Z, Zhao D F, Li J, et al. Rolling Bearing Fault Diagnosis Based on Local Mean Decomposition Multi-scale Fuzzy Entropy and Grey Similar Incidence[J]. Acta Metrologica Sinca, 2018, 39(2): 231-236.
[4]孟宗, 季艳, 谷伟明, 等. 基于支持向量机和窗函数的DEMD端点效应抑制方法[J]. 计量学报, 2016, 37(2): 180-184.
Meng Z, Ji Y, Gu W M, et al. End effects restraining of DEMD based on support vector machine and window function[J]. Acta Metrologica Sinca, 2016, 37(2): 180-184.
[5]Yang Y, Cheng J S, Zhang K. An ensemble local means decomposition method and its application to local rubimpact fault diagnosis of the rotor systems[J]. Mcasurcmcnt, 2012, 45(3): 561-570.
[6]张淑清, 邢婷婷, 何红梅, 等. 基于VMD及广义分形维数矩阵的滚动轴承故障诊断[J]. 计量学报, 2017, 38(4): 439-443.
Zhang S Q, Xing T T, He H M, et al. Bearing fault diagnosis method based on VMD and generalized fractal dimension matrix[J]. Acta Metrologica Sinca, 2017, 38(4): 439-443.
[7]王晓龙, 唐贵基. 基于变分模态分解和1. 5维谱的轴承早期故障诊断方法[J]. 电力自动化设备, 2016, 36(7): 125-130.
Wang X L, Tang G J. Incipient bearing fault diagnosis based on VMD and 1. 5-dimension spectrum[J]. Electric Power Automation Equipment, 2016, 36(7): 125-130.
[8]马洪斌, 佟庆彬, 张亚男. 优化参数的变分模态分解在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2018, 29(4): 390-397.
Ma H B, Tong Q B, Zhang Y N. Applications of optimization parameters VMD to fault diagnosis of rolling bearings[J]. China Mechanical Engineering, 2018, 29(4): 390-397.
[9]唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报, 2015, 49(5): 73-81.
Tang G J, Wang X L. Parameter Optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi’an Jiaotong University, 2015, 49(5): 73-81.
[10]郑慧峰, 喻桑桑, 王月兵, 等. 基于经验模态分解和奇异值分解的振动声调制信号分析方法研究[J]. 计量学报, 2016, 37(4): 398-401.
Zheng H F, Yu S S, Wang Y B, et al. Research on the analysis method of vibro-acoustic modulation signal based on EMD and SVD[J]. Acta Metrologica Sinca, 2016, 37(4): 398-401.
[11]刘英杰, 范玉刚, 吴建德. 基于SVD和SVDD的轴承故障诊断[J]. 控制工程, 2018, 25(3): 423-427.
Liu Y J, Fan Y G, Wu J D. Bearing fault diagnosis based on SVD and SVDD[J]. Control Engineering of China, 2018, 25(3): 423-427.
[12]孟宗, 谷伟明, 胡猛, 等. 基于改进奇异值分解和经验模式分解的滚动轴承早期微弱故障特征提取[J]. 计量学报, 2016, 37(4): 406-410.
Meng Z, Gu W M, Hu M, et al. Fault feature extraction of rolling bearing incipient fault based on inproved singular value decomposition and EMD[J]. Acta Metrologica Sinca, 2016, 37(4): 406-410.
[13]Wang Y, Markert R, Xiang J, et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J]. Mechanical Systems and Signal Processing, 2015, 60: 243-251.
[14]Dragomiretskiy K, Zosso D. Variational Mode Decomposition [J]. Signal Processing IEEE Transactions on, 2014, 62(3): 531-544.
[15]Pustelnik N, Borgnat P, Flandrin P. A multicomponent proximal algorithm for Empirical Mode Decomposition [C]// Signal Processing Conference IEEE, 2012, 1880-1884.
[16]朱坚民, 赵全龙, 何丹丹. 基于奇异值分解和灰靶决策的车刀磨损状态判别[J]. 计量学报, 2017, 38(2): 189-192.
Zhu J M, Zhao Q L, He D D. Wear condition recognition of lathe tool based on singular value decomposition and grey target decision methods[J]. Acta Metrologica Sinca, 2017, 38(2): 189-192.
[17]孟宗,殷娜,李晶. 基于信号稀疏表示和瞬态冲击信号多特征提取的滚动轴承故障诊断[J]. 计量学报, 2019, 40(5): 855-860.
Meng Z, Yin N, Li J. Fault Diagnosis of Rolling Bearing Based on Sparse Representation of Signals and Transient Impulse Signal Multifeature Extraction[J]. Acta Metrologica Sinca, 2019, 40(5): 855-860. |
|
|
|