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浙江大学学报(工学版)  2019, Vol. 53 Issue (11): 2092-2101    DOI: 10.3785/j.issn.1008-973X.2019.11.006
机械工程     
基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取
赵祥龙1(),陈捷1,2,*(),洪荣晶1,2,王华1,2,李媛媛3
1. 南京工业大学 机械与动力工程学院,江苏 南京 211816
2. 江苏省工业装备数字制造及控制技术重点实验室,江苏 南京 211816
3. 敏实集团,浙江 宁波 315806
Adaptive feature extraction method for slewing bearing based on Wavelet leader and optimized isometric mapping method
Xiang-long ZHAO1(),Jie CHEN1,2,*(),Rong-jing HONG1,2,Hua WANG1,2,Yuan-yuan LI3
1. College of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China
2. Jiangsu Key Laboratory of Digital Manufacturing for Industrial Equipment and Control Technology, Nanjing 211816, China
3. Minth Group, Ningbo 315806, China
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摘要:

为了解决回转支承振动信号微弱,特征信息不易提取的问题,提出基于Wavelet leader方法和经混合灰狼算法优化的等距映射算法(HGWO-ISOMAP)的多分形自适应特征提取方法. 利用Wavelet leader计算多分形特征,挖掘振动数据的几何结构信息,构造高维特征矩阵;通过HGWO优化后的ISOMAP算法对高维特征矩阵进行自适应特征筛选;将筛选后的特征矩阵输入到经遗传算法(GA)优化的最小二乘支持向量机(LSSVM)中进行故障状态识别. 为了验证所提方法的优越性,采用课题组自主研发的回转支承综合性能试验台对某型号回转支承进行全寿命实验. 结果表明,相比一般时域、时频域、频域特征提取方法,所提方法能提高识别精度,缩短计算时间,为回转支承特征提取提供新的有效途径.

关键词: 回转支承特征提取多分形特征Wavelet leader混合灰狼优化算法(HGWO)等距映射(ISOMAP)    
Abstract:

Multi-fractal adaptive feature extraction method based on Wavelet leader method and isometric mapping method optimized by hybrid grey wolf optimization algorithm (HGWO-ISOMAP) was proposed, in order to solve the problem that the vibration signal of slewing bearing is weak and the feature information is difficult to extract. Wavelet leader is utilized to calculate the multi-fractal features, mine the geometric structure information of vibration data, and construct a high-dimensional multi-fractal feature matrix. Adaptive feature selection of high-dimensional feature matrix is carried out through ISOMAP method optimized by HGWO. The selected feature matrix is input into the least squares support vector machine (LSSVM) optimized by genetic algorithm (GA) for fault state identification. A full life experiment of a certain type of slewing bearing was carried out by using self-developed comprehensive performance test platform of slewing bearing, in order to verify the superiority of the proposed method. Results show that compared with general time domain, time-frequency domain and frequency domain feature extraction methods, the proposed method can improve the recognition accuracy and reduce the calculation time, providing a new effective way for feature extraction of slewing bearing.

Key words: slewing bearing    feature extraction    multi-fractal feature    Wavelet leader    hybrid grey wolf optimization algorithm (HGWO)    isometric mapping (ISOMAP)
收稿日期: 2018-09-11 出版日期: 2019-11-21
CLC:  TH 133  
基金资助: 国家自然科学基金资助项目(51875273);2014年度高校"青蓝工程"中青年学术带头人资助项目
通讯作者: 陈捷     E-mail: 2390698155@qq.com;nj_tech_cj@163.com
作者简介: 赵祥龙(1992—),男,硕士生,从事设备故障诊断研究. orcid.org/0000-0001-9464-973X. E-mail: 2390698155@qq.com
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引用本文:

赵祥龙,陈捷,洪荣晶,王华,李媛媛. 基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取[J]. 浙江大学学报(工学版), 2019, 53(11): 2092-2101.

Xiang-long ZHAO,Jie CHEN,Rong-jing HONG,Hua WANG,Yuan-yuan LI. Adaptive feature extraction method for slewing bearing based on Wavelet leader and optimized isometric mapping method. Journal of ZheJiang University (Engineering Science), 2019, 53(11): 2092-2101.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.11.006        http://www.zjujournals.com/eng/CN/Y2019/V53/I11/2092

图 1  回转支承自适应特征提取方法流程图
图 2  回转支承试验台实物图
图 3  回转支承试验台系统结构流程图
参数 数值 参数 数值
滚道中心直径/mm 1 000 滚珠数目 71
齿数 96 螺栓个数 36
滚珠直径/mm 40 轴/径向间隙/mm 0.05~0.20
外圈外径/mm 1 185.6 内圈内径/mm 878
钢球材料 GCr15 内/外圈材料 42GMo
表 1  某型号回转支承的结构参数
图 4  回转支承加速度信号图
图 5  振动信号降噪前后对比图
图 6  3种状态下的多分形特征图
图 7  单个样本的多分形谱-奇异指数图
图 8  3种状态下的特殊点分布统计结果
状态 初始点范围 最高点范围 终止点范围
正常状态 0.05~0.15 0.22~0.29 0.32~0.40
螺栓破坏 0.04~0.07 0.07~0.10 0.12~0.20
外圈破坏 0.35~0.54 0.57~0.65 0.72~0.88
表 2  特殊点分布区间统计结果
图 9  HGWO-ISOMAP方法的降维结果
图 10  降维后的数据可视化
图 11  降维后任选三维数据的可视化
图 12  LS-SVM方法的分类结果
分类器 方法 Rc/% t/s
LSSVM HGWO-ISOMAP-Wavelet leader 99.33 361.453
降维前-Wavelet leader 96.67 560.643
f1-f2-f3 98.00 374.419
f1-f2-f4 98.67 360.888
f2-f3-f4 88.00 366.519
表 3  本研究所提方法的识别结果
分类器 方法 Rc/% t /s
LSSVM 时域特征 85.33 377.801
频域特征 88.00 366.634
时频域特征 89.33 352.214
时域-频域-时频域混合特征 92.00 417.547
BP神经网络 HGWO-ISOMAP-Wavelet leader 88.67 30.158
降维前-Wavelet leader 82.67 217.013
f1-f2-f3 86.00 28.765
f1-f2-f4 88.45 29.127
f2-f3-f4 67.33 29.333
时域特征 66.00 17.497
频域特征 66.67 21.377
时频域特征 68.67 20.046
时域-频域-时频域混合特征 75.33 23.223
表 4  其他方法的识别结果
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