吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (2): 318-328.doi: 10.13229/j.cnki.jdxbgxb20200856

• 车辆工程·机械工程 • 上一篇    

基于自适应变分模态分解和集成极限学习机的滚动轴承多故障诊断

王进花1,2,3(),胡佳伟1,曹洁1,4,黄涛5   

  1. 1.兰州理工大学 电气工程与信息工程学院，兰州 730050
    2.兰州理工大学 甘肃工业过程先进控制重点实验室，兰州 730050
    3.兰州理工大学 电气与控制工程国家实验教学中心，兰州 730050
    4.甘肃省制造信息工程研究中心，兰州 730050
    5.中国市政工程西北设计研究院有限公司，兰州 730000
  • 收稿日期:2020-11-06 出版日期:2022-02-01 发布日期:2022-02-17
  • 作者简介:王进花（1976-），女，副教授，博士.硕士生导师.研究方向：智能信息处理，多源信息融合，复杂系统建模与仿真.E-mail：wjh0615@lut.edu.cn
  • 基金资助:
    国家自然科学基金项目(61763028);甘肃省自然科学基金项目(20JR5RA463)

Multi⁃fault diagnosis of rolling bearing based on adaptive variational modal decomposition and integrated extreme learning machine

Jin-hua WANG1,2,3(),Jia-wei HU1,Jie CAO1,4,Tao HUANG5   

  1. 1.College of Electrical & Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China
    2.Key Laboratory of Gansu Advanced Control for Industrial Processes，Lanzhou University of Technology，Lanzhou 730050，China
    3.National Experimental Teaching Center of Electrical and Control Engineering，Lanzhou University of Technology，Lanzhou 730050，China
    4.Engineering Research Center of Manufacturing Information of Gansu Province，Lanzhou 730050，China
    5.China Municipal Engineering Northwest Design and Research Institute Co. ，Ltd. ，Lanzhou 730000，China
  • Received:2020-11-06 Online:2022-02-01 Published:2022-02-17

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摘要：

针对滚动轴承多故障诊断中特征提取困难和分类准确性低的问题，从有效特征提取和故障分类准确性两方面出发，将变分模态分解（VMD）和极限学习机（ELM）方法结合，提出了一种自适应滚动轴承多故障诊断方法。针对VMD参数需人为事先设定导致信号分解效果差的情况，提出了灰狼算法（GWO）优化VMD实现自适应地获取最佳分解参数k和α。进一步，为了克服单个ELM模型分类精度不高和分类结果不稳定的问题，提出集成极限学习机（IELM）实现多故障的分类和识别，提高故障分类的准确性和稳定性。首先，采用GWO优化VMD，自适应地获取最佳分解参数；其次，选择并提取模态信号的时频特征向量；最后，将特征向量输入到IELM中进行训练和分类。实验表明：本文方法可以自适应地分解信号并产生最佳分解效果，实现滚动轴承故障的准确早期预警和识别。

关键词: 故障诊断, 灰狼优化算法, 变分模态分解, 集成极限学习机, 滚动轴承

Abstract:

In view of the difficulty of feature extraction and low classification accuracy in the diagnosis of rolling bearing multiple faults， this paper starts from the two aspects of effective feature extraction and fault classification accuracy， and combines the method of variational modal decomposition （VMD） and extreme learning machine （ELM）. An adaptive method for diagnosing multiple faults of rolling bearings is presented. Aiming at the situation that VMD parameters need to be manually set in advance， which leads to poor signal decomposition， the Gray Wolf Algorithm （GWO） is proposed to optimize VMD to achieve adaptively obtaining the best decomposition parameters k and α. Furthermore， in order to overcome the problem of low classification accuracy of a single ELM model and unstable classification results， an integrated extreme learning machine （IELM） is proposed to realize the classification and recognition of multiple faults， and improve the accuracy and stability of fault classification. First， use GWO to optimize VMD and obtain the best decomposition parameters adaptively； Secondly， select and extract the time-frequency feature vector of the modal signal； Finally， input the feature vector into IELM for training and classification. Experiments show that this method can adaptively decompose signals and produce the best decomposing effect， realizing accurate early warning and identification of rolling bearing faults.

Key words: fault diagnosis, gray wolf optimization, variational mode decomposition, integrated extreme learning machine, rolling bearing

中图分类号: 

  • TP277

图1

GWOVMD算法流程图"

图2

仿真信号图"

图3

EMD分解的模态"

图4

EEMD分解的模态"

图5

GWO优化VMD分解的模态"

图6

基于自适应VMD和IELM的滚动轴承故障诊断流程图"

图7

测试试验台"

表1

数据集"

数据集类型负载/HP转速/（r·min-1）故障尺寸/cm
A正常217500.017 78
内圈
滚动体
外圈
B内圈017970.017 78
11772
21750
31730
C内圈217500.017 78
0.035 56
0.053 34
0.071 12

表2

GWOVMD分解最佳参数"

状态kα
正常94860
内圈4585
滚动体65170
外圈106726

表3

相关系数筛选模态信号"

项目正常内圈外圈滚动体
IMF10.510.270.050.13
IMF20.290.410.080.24
IMF30.780.660.070.47
IMF40.220.630.290.59
IMF50.09-0.370.67
IMF60.05-0.390.16
IMF70.04-0.65-
IMF80.03-0.62-
IMF90.03-0.08-
IMF10--0.08-

图8

数据集测试样本分类正确率"

表4

五个故障诊断方法的准确性"

方法数据集A数据集B数据集C
EEMD-ELM93.7591.2588.75
EEMD-IELM98.7597.5093.75
自适应VMD-ELM97.5096.2590.00
文献［30］98.7596.2590.00
自适应VMD-IELM100.00100.0098.75
1 Amirat Y, Benbouzid M E H, Al-Ahmar E, et al. A brief status on condition monitoring and fault diagnosis in wind energy conversion systems[J]. Renewable & Sustainable Energy Reviews, 2009, 13(9): 2629-2636.
2 Tian Z, Jin T, Wu B, et al. Condition based maintenance optimization for wind power generation systems under continuous monitoring[J]. Renewable Energy, 2011, 36(5): 1502-1509.
3 Umamaheswari R, Maheswari R U. Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train—a contemporary survey[J]. Mechanical Systems & Signal Processing, 2017, 85: 296-311
4 Fan J, Zhu Z C, Wei L. An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing[J]. IEEE Access, 2018, 6: 44483-44493.
5 Pang B, Tang G, Tian T, et al. Rolling bearing fault diagnosis based on an improved HTT transform[J]. Sensors, 2018, 18(4): 1203-1211.
6 Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings Mathematical Physical & Engineering Ences, 1998, 454(1971): 903-995.
7 Liu B, Riemenschneider S, Xu Y. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum[J]. Mechanical Systems & Signal Processing, 2006, 20(3): 718-734.
8 Smith J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface, 2005, 2(5): 443-454.
9 Liu W Y, Gao Q W, Ye G, et al. A novel wind turbine bearing fault diagnosis method based on integral extension LMD[J]. Measurement, 2015, 74: 70-77.
10 Gao W, Wai R J, Qiao S P, et al. Mechanical faults diagnosis of high-voltage circuit breaker via hybrid features and integrated extreme learning machine[J], IEEE Access, 2019, 7: 60091-60103.
11 黄鑫, 张小栋, 刘洪成, 等. 涡轮叶片早期裂纹的三维叶尖间隙EEMD能量熵融合诊断方法[J]. 航空动力学报, 2020, 35(5): 918-927.
Huang Xin, Zhang Xiao-dong, Liu Hong-cheng, et al. Approach to early crack diagnosis of turbine blade based on EEMD energy entropy fusion of three-dimensional tip clearance[J]. Journal of Aerospace Power, 2020, 5: 918-927.
12 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.
13 徐艳春, 高永康, 李振兴, 等. 改进LMD算法在微电网电能质量扰动信号检测中的应用[J]. 电网技术, 2019, 43(1): 332-339.
Xu Yan-chun, Gao Yong-kang, Li Zhen-xing, et al. Application of improved LMD algorithm in signal detection of power quality disturbance in microgrid[J]. Power System Technology, 2019, 43(1): 332-339.
14 Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
15 刘秀丽, 徐小力, 吴国新, 等. 基于变分模态分解的故障弱信息提取方法[J]. 华中科技大学学报: 自然科学版, 2020, 48(7): 117-121.
Liu Xiu-li, Xu Xiao-li, Wu Guo-xin, et al. Extraction method of weak fault information based on variational mode decomposition[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2020, 48(7): 117-121.
16 姚晰童, 代煜, 张建勋, 等. 陡脉冲干扰下的心电信号滤波及QRS提取[J]. 工程科学学报, 2020, 42(5): 654-662.
Yao Xi-tong, Dai Yu, Zhang Jian-xun, et al. ECG filtering and QRS extraction under steep pulse interference[J]. Chinese Journal of Engineering, 2020, 42(5): 654-622.
17 李华, 伍星, 刘韬, 等. 基于信息熵优化变分模态分解的滚动轴承故障特征提取[J]. 振动与冲击, 2018, 37(23): 219-225.
Li Hua, Wu Xing, Liu Tao, et al. Bearing fault feature extraction based on VMD optimized with information entropy[J]. Journal of Vibration and Shock, 2018, 37(23): 219-225.
18 谷然, 陈捷, 洪荣晶, 等. 基于改进自适应变分模态分解的滚动轴承微弱故障诊断[J]. 振动与冲击, 2020, 39(8): 1-7, 22.
Gu Ran, Chen Jie, Hong Rong-jing, et al. Early fault diagnosis of rolling bearings based on adaptive variational mode decomposition and the Teager energy operator[J]. Journal of Vibration and Shock, 2020, 39(8): 1-7, 22.
19 刘建昌, 权贺, 于霞, 等. 基于参数优化VMD和样本熵的滚动轴承故障诊断[J/OL]. [2020-08-27].
20 焦博隆, 钟志贤, 刘翊馨, 等. 基于蝙蝠算法优化的变分模态分解的转子裂纹检测方法[J]. 振动与冲击, 2020, 39(6): 98-103, 124.
Jiao Bo-long, Zhong Zhi-xian, Liu Yi-xin, et al. Rotor crack detection method based on variational mode decomposition based on optimization parameters of bat algorithm[J]. Journal of Vibration and Shock, 2020, 39(6): 98-103, 124.
21 Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
22 Gu R, Chen J, Hong R J, et al. Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator[J]. Measurement, 2020, 14: 106941.
23 Huang G B, Zhou H, Ding X, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems Man & Cybernetics Part B, 2012, 42(2): 513-529.
24 Zhao Z, Chen Z, Chen Y, et al. A class incremental extreme learning machine for activity recognition[J]. Cognitive Computation, 2014, 6(3): 423-431.
25 Laddada S, Si-Chaib M O, Benkedjouh T, et al. Tool wear condition monitoring based on wavelet transform and improved extreme learning machine[J]. ARCHIVE Proceedings of the Institution of Mechanical Engineers Part C, Journal of Mechanical Engineering Science, 2020, 234(5): 095440621988854.
26 Qiao S P, Gao W, Wai R J, et al. A method of mechanical fault feature extraction for high-voltage circuit breaker via CEEMDAN and weighted time-frequency entropy[C]∥4th International Conference on Intelligent Green Building and Smart Grid, Yichang, 2019: 25-29.
27 Chen X J, Yang Y M, Cui Z X, et al. Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy[J]. Energy, 2019, 174: 1100-1109.
28 Zhang X L, Yan Q, Yang J, et al. An assembly tightness detection method for bolt-jointed rotor with wavelet energy entropy[J]. Measurement, 2019, 136:212-244.
29 Li J C. A novel recognition algorithm based on holder coefficient theory and interval gray relation classifier[J]. Ksii Transactions on Internet & Information Systems, 2015, 9(11): 4573-4584.
30 Li H, Fan B, Jia R, et al. Research on multi-domain fault diagnosis of gearbox of wind turbine based on adaptive variational mode decomposition and extreme learning machine algorithms[J]. Energies, 2020, 13(6): 1-20.
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