基于VMD和改进多分类马田系统的滚动轴承故障智能诊断

PDF(1151 KB)

振动与冲击 ›› 2020, Vol. 39 ›› Issue (2) : 32-39.

论文

詹君，程龙生，彭宅铭

作者信息 +

Intelligent fault diagnosis of rolling bearings based on the VMD and improved-multi-classification Mahalanobis Taguchi system

ZHAN Jun，CHENG Longsheng，PENG Zhaiming

Author information +

文章历史 +

摘要

为了有效提取滚动轴承的故障信号，选择合适的智能分类器识别故障状态，提出基于变分模态分解及多重马氏距离法的多分类马田系统的故障智能诊断系统。通过变分模态分解将振动信号分解为多个本征模函数并提取相关特征；并采用了多重马氏距离法的马田系统，以特征子集代替特征参与分类器的构建，以解决特征参数众多的问题；通过正交表和信噪比，筛选出各状态的敏感模态分量，并提出多分类马田系统，用于多类故障智能识别；将其应用于滚动轴承故障数据中，验证算法的有效性，并与其他算法对比分析。结果表明，基于变分模态分解及改进的多分类马田系统算法能简化诊断系统、训练耗时少，识别准确率高，是一种更为有效的故障智能诊断方法。

Abstract

In order to effectively extract the fault signal of rolling bearings and to select a suitable intelligent classifier for the identification of fault types, the variational mode decomposition and multi-classification Mahalanobis Taguchi system with multiple Mahalanobis distance were proposed to construct an intelligent fault diagnosis system.Vibration signals were decomposed into several intrinsic mode functions by the variational mode decomposition.Features of each mode function were extracted.To deal with the issue of excessive number of features in diagnosis system, the Mahalanobis Taguchi system was adopted based on multiple Mahalanobis distances.In stead of individual features, feature subsets were used in participating in the construction of the classifier.Orthogonal arrays and signal-to-noise ratios were used to select key intrinsic mode functions and the multi-classification Mahalanobis Taguchi system was utilized to diagnose faults intelligently.To verify the validity of the method, rolling bearing fault data were tested and the results were compared with other intelligent algorithms.The results indicate that the proposed algorithm is of advantages of algorithm higher accuracy, simplified diagnostic complexity, and decreased training time.It is an efficient intelligent fault diagnosis method.

导出引用

詹君，程龙生，彭宅铭. 基于VMD和改进多分类马田系统的滚动轴承故障智能诊断[J]. 振动与冲击, 2020, 39(2): 32-39

ZHAN Jun，CHENG Longsheng，PENG Zhaiming. Intelligent fault diagnosis of rolling bearings based on the VMD and improved-multi-classification Mahalanobis Taguchi system[J]. Journal of Vibration and Shock, 2020, 39(2): 32-39

参考文献

[1] LI K, SU L, WU J, et al. A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine [J]. Applied Sciences, 2017, 7(10): 1004.
[2] LIU Y, HE B, LIU F, et al. Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification [J]. Journal of Sound and Vibration, 2016, 385: 389-401.
[3] HUANG Y, LIN J, LIU Z, et al. A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis [J]. Journal of Sound and Vibration, 2019, 444: 216-234.
[4] AN XL, TANG YJ. Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine [J]. Transactions of the Institute of Measurement and Control, 2016, 39(7): 1000-1006.
[5] YAN XA, JIA MP. Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings [J]. Mechanical Systems and Signal Processing, 2019, 122: 56-86.
[6] ZHANG S, WANG Y, HE S, et al. Bearing fault diagnosis based on variational mode decomposition and total variation denoising [J]. Measurement Science and Technology, 2016, 27(7), 075101.
[7] HUANG N E，SHEN Z，LONG S Ｒ，et al． The empirical mode decomposition and the Hilbert spectrum for non-liner and non-stationary time series analysis [J]. Proceedings of the Royal Society, 1998, 454: 903-993.
[8] WU Z H， HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[9] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition [J]. IEEE Transactions on Signal Processing, 2014，62(3): 531-544.
[10] 任学平, 李攀, 王朝阁, 等. 基于改进 VMD与包络导数能量算子的滚动轴承早期故障诊断[J]. 振动与冲击, 2018, 37 (15): 6-13.
REN Xue-ping, LI Pan, WANG Chao-ge, et al. ZHANG Chao. Rolling bearing early fault diagnosis based on improved VMD and envelope derivative operator [J]. Journal of Vibration and Shock, 2018, 37(15): 6-13.
[11] 戚晓利, 叶绪丹, 蔡江林, 等. 基于变分模态分解与流形学习的滚动轴承故障特征提取方法[J]. 振动与冲击, 2018, 37(23): 133-140.
QI Xiao-li, YE Xu-dan, CAI Jiang-lin, et al. Fault feature extraction method of rolling bearings based on VMD and manifold learning [J]. Journal of Vibration and Shock, 2018, 37(23): 133-140.
[12] 马增强, 李亚超, 刘政, 等. 基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J]. 振动与冲击, 2016, 35(13): 134-139.
MA Zeng-qiang, LI Ya-chao, LIU Zheng, et al. Rolling bearings' fault feature extraction based on variational mode decomposition and Teager energy operator [J]. Journal of Vibration and Shock, 2016, 35(13): 134-139.
[13] 陈东宁, 张运东, 姚成玉, 等. 基于变分模态分解和多尺度排列熵的故障诊断[J]. 计算机集成制造系统, 2017, 23(12): 2604-2612.
CHEN Dong-ning, ZHANG Yun-dong, YAO Cheng-yu, et al. Fault diagnosis method based on variational mode decomposition and multi-scale permutation entropy [J]. Computer Integrated Manufacturing Systems, 2017, 23(12): 2604-2612.
[14] 郑小霞, 周国旺, 任浩翰, 等. 基于变分模态分解和排列熵的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(22): 22-28.
ZHENG Xiao-xia, ZHOU Guo-wang, REN Hao-han, et al. A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy [J]. Journal of Vibration and Shock, 2017, 36(22): 22-28.
[15] 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 Gui-ji, WANG Xiao-long. 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．
[16] 雷亚国, 贾峰, 孔德, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104.
LEI Ya-guo, JIA Feng, KONG Detong, et al. Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era [J]. Journal of Mechanical Engineering, 2018, 54(5): 94-104.
[17] SUN JD, YAN CH, WEN JT. Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning [J]. IEEE transaction on instrumentation and measurement, 2018, 67(1): 185-195.
[18] 王新, 闫文源. 基于变分模态分解和 SVM 的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(18): 252-256.
WANG Xin，YAN Wen-yuan. Fault diagnosis of roller bearings based on the variational mode decomposition and SVM [J]. Journal of Vibration and Shock, 2017, 36(18): 252-256.
[19] PATIL AB, GAIKWAD JA, KULKARNI JV. Bearing fault diagnosis using discrete wavelet transform and artificial neural network [J]. International Conference on Applied and Theoretical Computing and Communication Technology, 2016: 399-405.
[20] HAN T, JIANG D. Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier [J]. Shock and Vibration, 2016: 1-11.
[21] TAGUCHI G, JUGULUM R. New trends in multivariate diagnosis [J]. Sankhyã: The Indian Journal of Statistics, 2000, 62 (Series B): 233-248.
[22] TAGUCHI G, JUGULUM R. The Mahalanobis-Taguchi strategy--A pattern technology system [M]. Japan: John Wiley and Sons, 2002.
[23] CHANG ZP, LI YW, FATIMA N. A Theoretical Survey on Mahalanobis-Taguchi System [J]. Measurement, 2019, 136: 501- 510.
[24] SOYLEMEZOGLU A, JAGANNATHAN S, SAYGIN C. Mahalnobis Taguchi System (MTS) as a prognostic tool for rolling element bearing failures [J]. Journal of Manufacturing Science and Engineering, 2010, 132(5): 635-645.
[25] SHAKYA P, KULKARNI MS, DARPE AK. Bearing diagnosis based on Mahalanobis-Taguchi-Gram-Schmidt method [J]. Journal of Sound and Vibration, 2015, 337: 342-362.
[26] CHEN JX, CHENG LS, YU H, et al. Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis-Taguchi system [J]. International Journal of Systems Science, 2018, 49(1): 147-159.
[27] 李兆飞, 柴毅, 李华锋. 多重分形去趋势波动分析的振动信号故障诊断[J]. 华中科技大学学报, 2012, 40(12): 5-9.
LI Zhao-fei, CHAI Yi, LI Hua-feng. Diagnosing faults in vibration signals by multi-gractal detrended fluctuation analysis [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2012, 40(12): 5-9.
[28] 生志荣, 程龙生, 顾玉萍. 基于控制图的马田系统马氏空间生成机理研究[J].数理统计与管理, 2017, 36(6): 1059-1068. SHENG Zhi-rong, CHENG Long-sheng, GU Yu-ping. Generation Mechanism on Mahalanobis Space of MTS Based on the Control Chart [J]. Journal of Applied Statistics and Management, 2017, 36(6): 1059-1068.
[29] LOPARO KA. Bearings vibration data set, Case Western Reserve University. http://csegroups.case.edu/bearingdatacenter/ pages/download-data-file.
[30] 剡昌锋, 朱涛, 吴黎晓, 等. 基于马田系统的滚动轴承初始故障检测和状态监测[J]. 振动与冲击, 2017, 36(12): 155-162.
YAN Chang-feng, ZHU Tao, WU Li-xiao, et al. Incipient fault detection and condition monitoring of rolling bearings by using the Mahalanobis-Taguchi System [J]. Journal of Vibration and Shock, 2017, 36(12): 155-162.