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基于联合LLE和SSR的滚动轴承故障诊断方法

张康智

张康智. 基于联合LLE和SSR的滚动轴承故障诊断方法[J]. 航空动力学报, 2024, 39(7):20230263 doi: 10.13224/j.cnki.jasp.20230263
引用本文: 张康智. 基于联合LLE和SSR的滚动轴承故障诊断方法[J]. 航空动力学报, 2024, 39(7):20230263 doi: 10.13224/j.cnki.jasp.20230263
ZHANG Kangzhi. Fault diagnosis method of rolling bearing based on joint LLE and SSR[J]. Journal of Aerospace Power, 2024, 39(7):20230263 doi: 10.13224/j.cnki.jasp.20230263
Citation: ZHANG Kangzhi. Fault diagnosis method of rolling bearing based on joint LLE and SSR[J]. Journal of Aerospace Power, 2024, 39(7):20230263 doi: 10.13224/j.cnki.jasp.20230263

基于联合LLE和SSR的滚动轴承故障诊断方法

doi: 10.13224/j.cnki.jasp.20230263
基金项目: 陕西省科技厅项目(2019JM-535); 陕西省自然科学基础研究计划项目(2023-JC-QN-0614)
详细信息
    作者简介:

    张康智(1978-),男,副教授,硕士,主要从事机电液一体化技术的研究。E-mail:59851747@qq.com

  • 中图分类号: V231.92

Fault diagnosis method of rolling bearing based on joint LLE and SSR

  • 摘要:

    针对滚动轴承振动信号具有较强的非线性,且包含较多冗余和无关特征,导致提取本质特征和故障识别困难,提出一种基于联合局部线性嵌入和稀疏自表示(joint locally linear embedding and sparse self-representation, JLLESSR)与参数优化支持向量机的滚动轴承故障诊断方法。该方法构造了一个统一的特征提取框架,依靠局部线性嵌入(locally linear embedding,LLE)挖掘高维数据的局部几何结构,同时通过稀疏自表示 (self-representation) 在低维空间挖掘高维数据的全局几何结构,得到表征滚动轴承运行状态的嵌入特征。然后,将得到的特征输入至交叉优化支持向量机(cross-validation support vector machine,CV-SVM)中进行故障识别。最后,在常见滚动轴承故障数据集上对所提出的方法进行测试,实验结果表明提出的方法能有效识别出滚动轴承不同类型的故障,并且故障诊断精度可达98.5%。

     

  • 图 1  局部线性嵌入算法的流程图

    Figure 1.  Flowchart of local linear embedding algorithm

    图 2  滚动轴承故障诊断流程图

    Figure 2.  Flow chart of rolling bearing fault diagnosis

    图 3  滚动轴承故障模拟实验台

    Figure 3.  Rolling bearing failure simulation test bench

    图 4  三轴加速度传感器

    Figure 4.  Three-axis acceleration sensor

    图 5  实验轴承

    Figure 5.  Bearings used in experiment

    图 6  滚动轴承振动信号时域表示

    Figure 6.  Time domain representation of rolling bearing vibration signals

    图 7  不同类型的信号频谱图

    Figure 7.  Spectrum diagram of different types of signals

    图 8  不同特征提取算法的三维可视化结果

    Figure 8.  3-dimensional visualization results of different feature extraction algorithm

    图 9  不同参数下的轴承故障识别精度

    Figure 9.  Accuracy of bearing fault identification under different parameters

    表  1  轴承数据集的识别精度对比

    Table  1.   Comparison of identification accuracy of bearing dataset

    模型 识别精度/%
    预测精度 召回率 F1分数
    LLE-CV-SVM 91.15 90.85 91.25
    HLLE-CV-SVM 93.25 93.34 92.50
    RLLE-CV-SVM 94.05 93.85 94.25
    MS-LLECF-CV-SVM 95.15 94.35 95.00
    MS-LLEFF-CV-SVM 95.50 95.75 95.65
    JLLESSR-CV-SVM 98.75 98.60 98.50
    下载: 导出CSV
  • [1] 李昕燃,靳伍银. 基于改进麻雀算法优化支持向量机的滚动轴承故障诊断研究[J]. 振动与冲击,2023,42(6): 106-114. LI Xinran,JIN Wuyin. Fault diagnosis of rolling bearings based on ISSA-SVM[J]. Journal of Vibration and Shock,2023,42(6): 106-114. (in Chinese

    LI Xinran, JIN Wuyin. Fault diagnosis of rolling bearings based on ISSA-SVM[J]. Journal of Vibration and Shock, 2023, 42(6): 106-114. (in Chinese)
    [2] 李俊卿,胡晓东,耿继亚,等. 基于ACGAN和模型融合的电机轴承故障诊断方法[J]. 电机与控制应用,2023,50(2): 91-96. LI Junqing,HU Xiaodong,GENG Jiya,et al. Fault diagnosis method of motor bearing based on ACGAN and model fusion[J]. Electric Machines & Control Application,2023,50(2): 91-96. (in Chinese doi: 10.12177/emca.2022.157

    LI Junqing, HU Xiaodong, GENG Jiya, et al. Fault diagnosis method of motor bearing based on ACGAN and model fusion[J]. Electric Machines & Control Application, 2023, 50(2): 91-96. (in Chinese) doi: 10.12177/emca.2022.157
    [3] 殷海双,胡泽彪,刘远红,等. 基于鲁棒局部线性嵌入投票的轴承故障诊断[J]. 组合机床与自动化加工技术,2021(8): 81-84,89. YIN Haishuang,HU Zebiao,LIU Yuanhong,et al. Bearing fault diagnosis based on robust locally linear embedded vote[J]. Modular Machine Tool & Automatic Manufacturing Technique,2021(8): 81-84,89. (in Chinese

    YIN Haishuang, HU Zebiao, LIU Yuanhong, et al. Bearing fault diagnosis based on robust locally linear embedded vote[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(8): 81-84, 89. (in Chinese)
    [4] ROWEIS S T,SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science,2000,290(5500): 2323-2326. doi: 10.1126/science.290.5500.2323
    [5] WANG Xiang,ZHENG Yuan,ZHAO Zhenzhou,et al. Bearing fault diagnosis based on statistical locally linear embedding[J]. Sensors,2015,15(7): 16225-16247. doi: 10.3390/s150716225
    [6] ZHANG Yansheng,YE Dong,LIU Yuanhong,et al. Machinery fault diagnosis via an improved multi-linear subspace and locally linear embedding[J]. Transactions of the Institute of Measurement and Control,2018,40(14): 4014-4026. doi: 10.1177/0142331217739688
    [7] ZHANG Xingwu,YU Xiaolei,LIU Yilong,et al. Adaptive neighborhood selection based on locally linear embedding for the degradation index construction of traction motor bearing[J]. Measurement Science and Technology,2021,32(11): 115123. doi: 10.1088/1361-6501/ac18a5
    [8] LIU Guohong,LI Xiaomeng,WANG Cong,et al. Hessian locally linear embedding of PMU data for efficient fault detection in power systems[J]. IEEE Transactions on Instrumentation and Measurement,2022,71: 3502704.
    [9] ZHANG Yansheng,YE Dong,LIU Yuanhong. Robust locally linear embedding algorithm for machinery fault diagnosis[J]. Neurocomputing,2018,273: 323-332. doi: 10.1016/j.neucom.2017.07.048
    [10] LIU Yuanhong,HU Zebiao,ZHANG Yansheng. Bearing feature extraction using multi-structure locally linear embedding[J]. Neurocomputing,2021,428: 280-290. doi: 10.1016/j.neucom.2020.11.048
    [11] LIU Yuanhong,HU Zebiao,ZHANG Yansheng. Symmetric positive definite manifold learning and its application in fault diagnosis[J]. Neural Networks,2022,147: 163-174. doi: 10.1016/j.neunet.2021.12.013
    [12] 王贡献,张淼,胡志辉,等. 基于多尺度均值排列熵和参数优化支持向量机的轴承故障诊断[J]. 振动与冲击,2022,41(1): 221-228. WANG Gongxian,ZHANG Miao,HU Zhihui,et al. Bearing fault diagnosis based on multi-scale mean permutation entropy and parametric optimization SVM[J]. Journal of Vibration and Shock,2022,41(1): 221-228. (in Chinese

    WANG Gongxian, ZHANG Miao, HU Zhihui, et al. Bearing fault diagnosis based on multi-scale mean permutation entropy and parametric optimization SVM[J]. Journal of Vibration and Shock, 2022, 41(1): 221-228. (in Chinese)
    [13] 王一鹏,陈学振,李连玉. 基于小波包混合特征和支持向量机的机床主轴轴承故障诊断研究[J]. 电子测量与仪器学报,2021,35(2): 59-64. WANG Yipeng,CHEN Xuezhen,LI Lianyu. Research on fault diagnosis of machine spindle bearing based on wavelet packet mixing feature and SVM[J]. Journal of Electronic Measurement and Instrumentation,2021,35(2): 59-64. (in Chinese

    WANG Yipeng, CHEN Xuezhen, LI Lianyu. Research on fault diagnosis of machine spindle bearing based on wavelet packet mixing feature and SVM[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(2): 59-64. (in Chinese)
    [14] 史庆军,郭晓振,刘德胜. 基于特征量融合和支持向量机的轴承故障诊断[J]. 电子测量与仪器学报,2019,33(10): 104-111. SHI Qingjun,GUO Xiaozhen,LIU Desheng. Bearing fault diagnosis based on feature fusion and support vector machine[J]. Journal of Electronic Measurement and Instrumentation,2019,33(10): 104-111. (in Chinese

    SHI Qingjun, GUO Xiaozhen, LIU Desheng. Bearing fault diagnosis based on feature fusion and support vector machine[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(10): 104-111. (in Chinese)
    [15] HU Zebiao,YIN Haishuang,LIU Yuanhong. Locally linear embedding vote: a novel filter method for feature selection[J]. Measurement,2022,190: 110535. doi: 10.1016/j.measurement.2021.110535
    [16] 梁睿君,冉文丰,余传粮,等. 基于CWT-CNN的齿轮箱运行故障状态识别[J]. 航空动力学报,2021,36(12): 2465-2473. LIANG Ruijun,RAN Wenfeng,YU Chuanliang,et al. Recognition of gearbox operation fault state based on CWT-CNN[J]. Journal of Aerospace Power,2021,36(12): 2465-2473. (in Chinese

    LIANG Ruijun, RAN Wenfeng, YU Chuanliang, et al. Recognition of gearbox operation fault state based on CWT-CNN[J]. Journal of Aerospace Power, 2021, 36(12): 2465-2473. (in Chinese)
    [17] 蒋忆睿,裴洋,陈磊,等. 多局部约束自表示的谱聚类算法[J]. 计算机工程与应用,2020,56(11): 172-178. JIANG Yirui,PEI Yang,CHEN Lei,et al. Multiple locality-constrained self-representation for spectral clustering[J]. Computer Engineering and Applications,2020,56(11): 172-178. (in Chinese doi: 10.3778/j.issn.1002-8331.1904-0087

    JIANG Yirui, PEI Yang, CHEN Lei, et al. Multiple locality-constrained self-representation for spectral clustering[J]. Computer Engineering and Applications, 2020, 56(11): 172-178. (in Chinese) doi: 10.3778/j.issn.1002-8331.1904-0087
    [18] LIU Huawen,XU Xiaodan,LI Enhui,et al. Anomaly detection with representative neighbors[J]. IEEE Transactions on Neural Networks and Learning Systems,2023,34(6): 2831-2841. doi: 10.1109/TNNLS.2021.3109898
    [19] ABDI H,WILLIAMS L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews: Computational Statistics,2010,2(4): 433-459. doi: 10.1002/wics.101
    [20] 李红贤,韩延,吴敬涛,等. 基于ICA包络增强MEMD的滚动轴承故障诊断[J]. 航空动力学报,2021,36(2): 405-412. LI Hongxian,HAN Yan,WU Jingtao,et al. Rolling bearing fault diagnosis based on MEMD with ICA envelop enhancement[J]. Journal of Aerospace Power,2021,36(2): 405-412. (in Chinese

    LI Hongxian, HAN Yan, WU Jingtao, et al. Rolling bearing fault diagnosis based on MEMD with ICA envelop enhancement[J]. Journal of Aerospace Power, 2021, 36(2): 405-412. (in Chinese)
    [21] 余志锋,熊邦书,李新民,等. 基于改进的SqueezeNet直升机滚动轴承故障诊断[J]. 航空动力学报,2022,37(6): 1162-1170. YU Zhifeng,XIONG Bangshu,LI Xinmin,et al. Fault diagnosis of helicopter rolling bearing based on improved SqueezeNet[J]. Journal of Aerospace Power,2022,37(6): 1162-1170. (in Chinese

    YU Zhifeng, XIONG Bangshu, LI Xinmin, et al. Fault diagnosis of helicopter rolling bearing based on improved SqueezeNet[J]. Journal of Aerospace Power, 2022, 37(6): 1162-1170. (in Chinese)
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出版历程
  • 收稿日期:  2023-04-21
  • 网络出版日期:  2024-02-19

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