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时频分析和深度学习相结合的滚动轴承故障诊断

任胜杰 郭伟超 舒定真 汤奥斐 高新勤 李言

任胜杰, 郭伟超, 舒定真, 汤奥斐, 高新勤, 李言. 时频分析和深度学习相结合的滚动轴承故障诊断[J]. 机械科学与技术, 2023, 42(1): 149-158. doi: 10.13433/j.cnki.1003-8728.20200575
引用本文: 任胜杰, 郭伟超, 舒定真, 汤奥斐, 高新勤, 李言. 时频分析和深度学习相结合的滚动轴承故障诊断[J]. 机械科学与技术, 2023, 42(1): 149-158. doi: 10.13433/j.cnki.1003-8728.20200575
REN Shengjie, GUO Weichao, SHU Dingzhen, TANG Aofei, GAO Xinqin, LI Yan. Fault Diagnosis Method of Rolling Bearing Combining Time-frequency Analysis with Deep Learning[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 149-158. doi: 10.13433/j.cnki.1003-8728.20200575
Citation: REN Shengjie, GUO Weichao, SHU Dingzhen, TANG Aofei, GAO Xinqin, LI Yan. Fault Diagnosis Method of Rolling Bearing Combining Time-frequency Analysis with Deep Learning[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 149-158. doi: 10.13433/j.cnki.1003-8728.20200575

时频分析和深度学习相结合的滚动轴承故障诊断

doi: 10.13433/j.cnki.1003-8728.20200575
基金项目: 

国家自然科学基金项目 51505377

国家自然科学基金项目 51575443

陕西留学人员科技活动择优项目 302/253081605

陕西省教育厅协同创新中心项目 20JY047

详细信息
    作者简介:

    任胜杰(1997-), 硕士, 研究方向为故障诊断和深度学习, r1653482142@163.com

    通讯作者:

    郭伟超, 副教授, 硕士生导师, 博士, weichaoguo@xaut.edu.cn

  • 中图分类号: TH165.3;TH133.33

Fault Diagnosis Method of Rolling Bearing Combining Time-frequency Analysis with Deep Learning

  • 摘要: 滚动轴承大量使用在旋转机械中, 轴承的工况严重影响着机械设备的正常运行。为了提高轴承故障的诊断精度, 本文提出了一种时频分析和深度学习相结合的滚动轴承诊断方法。首先, 分析了十种不同时频分析方法; 其次, 建立了深度学习的滚动轴承故障诊断模型, 并利用迁移学习克服训练样本数量少的问题, 通过对比分析, 常数Q变换(Constant Q transform, CQT)的准确率可达100%;最后, 利用实验数据对所提方法的有效性和可靠性进行验证, 分别评估了在不同负载和噪声情况下的识别精度, 并与文献中的方法对比, 证明所提方法在不同工作环境条件下都有较好的鲁棒性和较高的识别精度。
  • 图  1  利用重叠采样进行数据增强

    图  2  故障信号和对应10种不同时频图

    图  3  卷积计算

    图  4  最大池化示例

    图  5  贝叶斯参数优化过程

    图  6  滚动轴承故障诊断流程

    图  7  轴承故障模拟实验台

    图  8  基于不同时频分析的迭代曲线

    图  9  CQT训练曲线

    图  10  CQT混淆矩阵

    图  11  不同数量训练样本识别结果

    图  12  t-SNE特征可视化

    图  13  不同负载域下的性能对比

    图  14  原始信号和加噪信号

    图  15  不同信噪比下的准确率对比

    表  1  采用的十种时频分析方法

    序号 时频分析方法 简写
    1 连续一维小波变换 CWT
    2 采用Hann窗的短时傅里叶变换 STFT-Hann
    3 采用Kaiser窗的短时傅里叶变换 STFT-Kaiser
    4 常数Q变换 CQT
    5 Hilbert-Huang变换 HHT
    6 傅立叶同步压缩变换 FSST
    7 Wigner-Ville分布 WVD
    8 估计瞬时频率 EIF
    9 可视化光谱峰度 VSK
    10 持久功率谱 PPS
    下载: 导出CSV

    表  2  滚动轴承数据集描述

    名称 正常 内圈故障 滚动体故障 外圈故障
    缺损凹坑直径/mm 0 0.18 0.36 0.53 0.18 0.36 0.53 0.18 0.36 0.53
    数据标签 Normal IR_18 IR_36 IR_53 Ball_18 Ball_36 Ball_53 OR_18 OR_36 OR_53
    数据集A 训练集 160 160 160 160 160 160 160 160 160 160
    测试集 40 40 40 40 40 40 40 40 40 40
    数据集B 训练集 160 160 160 160 160 160 160 160 160 160
    测试集 40 40 40 40 40 40 40 40 40 40
    数据集C 训练集 160 160 160 160 160 160 160 160 160 160
    测试集 40 40 40 40 40 40 40 40 40 40
    数据集D 训练集 160 160 160 160 160 160 160 160 160 160
    测试集 4 40 40 40 40 40 40 40 40 40
    下载: 导出CSV

    表  3  不同时频分析诊断结果

    时频分析方法 准确率/% 时间/s
    CWT 99.83 1 171
    STFT-Hann 99.50 4 796
    STFT-Kaiser 99.50 3 445
    CQT 100.00 1 608
    HHT 88.50 1 062
    FSST 99.75 1 444
    WVD 98.50 145
    EIF 99.40 3 881
    VSK 84.00 1 820
    PPS 99.75 2 120
    下载: 导出CSV

    表  4  针对负载适应性的数据设置

    描述 源域数据 目标域数据
    训练集B 测试集C 测试集D
    数据设置 训练集C 测试集B 测试集D
    训练集D 测试集B 测试集C
    下载: 导出CSV
  • [1] WANG Z J, ZHOU J, WANG J Y, et al. A novel fault diagnosis method of gearbox based on maximum kurtosis spectral entropy deconvolution[J]. IEEE Access, 2019, 7: 29520-29532 doi: 10.1109/ACCESS.2019.2900503
    [2] 雷亚国, 杨彬, 杜兆钧, 等. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报, 2019, 55(7): 1-8 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201907001.htm

    LEI Y G, YANG B, DU Z J, et al. Deep transfer diagnosis method for machinery in big data era[J]. Journal of Mechanical Engineering, 2019, 55(7): 1-8 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201907001.htm
    [3] ZHANG W, PENG G L, LI C H, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors, 2017, 17(2): 425 doi: 10.3390/s17020425
    [4] 孙国栋, 王俊豪, 徐昀, 等. CEEMD-WVD多尺度时频图像的滚动轴承故障诊断[J]. 机械科学与技术, 2020, 39(5): 688-694 doi: 10.13433/j.cnki.1003-8728.20190192

    SUN G D, WANG J H, XU Y, et al. Rolling bearing fault diagnosis based on CEEMD-WVD multi-scale time-frequency image[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(5): 688-694 (in Chinese) doi: 10.13433/j.cnki.1003-8728.20190192
    [5] DENG J, XU X Z, ZHANG Z X, et al. Universum autoencoder-based domain adaptation for speech emotion recognition[J]. IEEE Signal Processing Letters, 2017, 24(4): 500-504 doi: 10.1109/LSP.2017.2672753
    [6] WANG J Y, MO Z L, ZHANG H, et al. A deep learning method for bearing fault diagnosis based on time-frequency image[J]. IEEE Access, 2019, 7: 42373-42383 doi: 10.1109/ACCESS.2019.2907131
    [7] SARAVANAN N, RAMACHANDRAN K I. Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)[J]. Expert Systems with Applications, 2010, 37(6): 4168-4181 doi: 10.1016/j.eswa.2009.11.006
    [8] AMAR M, GONDAL I, WILSON C. Vibration spectrum imaging: a novel bearing fault classification approach[J]. IEEE Transactions on Industrial Electronics, 2015, 62(1): 494-502 doi: 10.1109/TIE.2014.2327555
    [9] GUO X J, CHEN L, SHEN C Q. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J]. Measurement, 2016, 93: 490-502 doi: 10.1016/j.measurement.2016.07.054
    [10] PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359 doi: 10.1109/TKDE.2009.191
    [11] SOHAIB M, KIM C H, KIM J M. A hybrid feature model and deep-learning-based bearing fault diagnosis[J]. Sensors, 2017, 17(12): 2876 doi: 10.3390/s17122876
    [12] LI X, JIA X D, ZHANG W, et al. Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation[J]. Neurocomputing, 2020, 383: 235-247 doi: 10.1016/j.neucom.2019.12.033
    [13] CHEN D M, YANG S, ZHOU F N. Incipient fault diagnosis based on dnn with transfer learning[C]//International Conference on Control, Automation and Information Sciences (ICCAIS). Hangzhou: IEEE, 2018: 303-308
    [14] 邵海东, 张笑阳, 程军圣, 等. 基于提升深度迁移自动编码器的轴承智能故障诊断[J]. 机械工程学报, 2020, 56(9): 84-90 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202009011.htm

    SHAO H D, ZHANG X Y, CHENG J S, et al. Intelligent fault diagnosis of bearing using enhanced deep transfer Auto-encoder[J]. Journal of Mechanical Engineering, 2020, 56(9): 84-90 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202009011.htm
    [15] ZHUANG Z L, LV H C, XU J, et al. A deep learning method for bearing fault diagnosis through stacked residual dilated convolutions[J]. Applied Sciences, 2019, 9(9): 1823 doi: 10.3390/app9091823
    [16] HOLIGHAUS N, DORFLER M, VELASCO G A, et al. A framework for invertible, real-time constant-Q transforms[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2013, 21(4): 775-785 doi: 10.1109/TASL.2012.2234114
    [17] OBERLIN T, MEIGNEN S, PERRIER V. The fourier-based synchrosqueezing transform[C]//2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Florence, Italy: IEEE, 2014
    [18] ANTONI J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 108-124 doi: 10.1016/j.ymssp.2005.12.002
    [19] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90 doi: 10.1145/3065386
    [20] TAN C Q, SUN F C, KONG T, et al. A survey on deep transfer learning[C]//27th Artificial Neural Networks and Machine Learning-ICANN 2018 Rhodes: Springer, 2018
    [21] 崔佳旭, 杨博. 贝叶斯优化方法和应用综述[J]. 软件学报, 2018, 29(10): 3068-3090 https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201810011.htm

    CUI J X, YANG B. Survey on bayesian optimization methodology and applications[J]. Journal of Software, 2018, 29(10): 3068-3090 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201810011.htm
    [22] LOU X S, LOPARO K A. Bearing fault diagnosis based on wavelet transform and fuzzy inference[J]. Mechanical Systems and Signal Processing, 2004, 18(5): 1077-1095 doi: 10.1016/S0888-3270(03)00077-3
    [23] SANTOS P, MAUDES J, BUSTILLO A. Identifying maximum imbalance in datasets for fault diagnosis of gearboxes[J]. Journal of Intelligent Manufacturing, 2018, 29(2): 333-351 doi: 10.1007/s10845-015-1110-0
    [24] VAN DER MAATEN L. Accelerating t-SNE using tree-based algorithms[J]. Journal of Machine Learning Research, 2014, 15(1): 3221-3245
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出版历程
  • 收稿日期:  2021-02-02
  • 刊出日期:  2023-01-25

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