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基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法

刘俊锋 董宝营 俞翔 万海波

刘俊锋, 董宝营, 俞翔, 等. 基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法[J]. 中国舰船研究, 2021, 17(X): 1–8 doi: 10.19693/j.issn.1673-3185.02158
引用本文: 刘俊锋, 董宝营, 俞翔, 等. 基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法[J]. 中国舰船研究, 2021, 17(X): 1–8 doi: 10.19693/j.issn.1673-3185.02158
LIU J F, DONG B Y, YU X, et al. Rolling bearing fault diagnosis method based on FSC-MPE and BP neural network[J]. Chinese Journal of Ship Research, 2021, 17(X): 1–8 doi: 10.19693/j.issn.1673-3185.02158
Citation: LIU J F, DONG B Y, YU X, et al. Rolling bearing fault diagnosis method based on FSC-MPE and BP neural network[J]. Chinese Journal of Ship Research, 2021, 17(X): 1–8 doi: 10.19693/j.issn.1673-3185.02158

基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法

doi: 10.19693/j.issn.1673-3185.02158
基金项目: 国家自然科学基金资助项目(51679245)
详细信息
    作者简介:

    刘俊锋,男,1997年生,硕士生。研究方向:机械故障诊断。E-mail:228302204@qq.com

    俞翔,男,1978年生,博士,副教授。研究方向:船舶工业。E-mail:yuxiang898@sina.com

    万海波,男,1987年生,博士,讲师。研究方向:船舶工业。E-mail:general3000@126.com

    通信作者:

    俞翔

  • 中图分类号: U664.21

Rolling bearing fault diagnosis method based on FSC-MPE and BP neural network

  • 摘要:   目的  为了从强背景噪声、非平稳、非线性的复杂设备滚动轴承早期冲击故障振动信号中有效提取故障特征并进行故障模式识别,  方法  首先利用快速谱相关分析对原始振动信号的故障特征进行提取,并利用多尺度排列熵对故障特征进行量化,然后将故障特征数据输入BP神经网络进行故障诊断模型训练与测试,最后,对变速情况下的滚动轴承故障模拟实验数据和美国凯斯西储大学的公开轴承故障试验数据集进行故障识别研究。  结果  结果显示:所提方法对不同类型的故障具有较高的辨识精度,可达97%以上。  结论  研究验证了基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法的可行性和优越性,可为滚动轴承健康状态评估提供技术支持。
  • 图  1  轴承故障诊断流程

    Figure  1.  Process of bearing fault diagnosis

    图  2  BP神经网络拓扑结构图

    Figure  2.  Topology structure diagram of BP neural network

    图  3  轴承故障模拟试验台

    Figure  3.  Bearing fault simulation test stand

    图  4  非平稳振动信号预处理

    Figure  4.  Non-stationary vibration signal preprocessing

    图  5  快速谱相关分析

    Figure  5.  Fast spectral coherence analysis

    图  6  增强包络谱

    Figure  6.  Enhanced envelope spectrum

    图  7  离散小波变换和EMD得到的故障特征

    Figure  7.  Fault characteristics obtained by DWT and EMD

    图  8  CWRU轴承故障试验台

    Figure  8.  Bearing failure test stand of CWRU

    图  9  试验轴承振动信号增强包络谱

    Figure  9.  Enhanced envelope spectrum of vibration signal of test bearing

    图  10  神经网络交叉熵损失与测试集识别准确率

    Figure  10.  Accuracy of neural network cross entropy loss and test set recognition

    图  11  测试集各类别故障识别结果

    Figure  11.  Various fault identification results of test sets

    表  1  轴承数据样本统计

    Table  1.   Sample statistics of bearing data

    故障类型损伤尺寸/in样本数量/个标签
    正常情况1500
    0.071501
    内圈故障0.141502
    0.211503
    0.071504
    外圈故障0.141506
    0.211507
    总计1 050
    下载: 导出CSV

    表  2  故障特征阶次

    Table  2.   Fault defect frequencies

    故障位置阶次
    内圈5.415 2
    外圈3.584 8
    下载: 导出CSV

    表  3  轴承故障诊断结果

    Table  3.   Diagnosis results of bearing fault

    故障类型损伤尺寸/in正确数/总数准确率/%
    正常情况25/25100
    0.0724/2596
    内圈故障0.1424/2596
    0.2125/25100
    0.0724/2596
    外圈故障0.1424/2596
    0.2125/25100
    总计171/17597.71
    下载: 导出CSV
  • [1] 杨大春. 基于集合经验模态分解的滚动轴承振动信号希尔伯特谱分析方法[J]. 机械制造, 2019, 57(8): 29–32, 79. doi: 10.3969/j.issn.1000-4998.2019.08.009

    YANG D C. Hilbert spectrum analysis method of rolling bearing vibration signal based on set empirical mode decomposition[J]. Machinery, 2019, 57(8): 29–32, 79 (in Chinese). doi: 10.3969/j.issn.1000-4998.2019.08.009
    [2] 彭畅. 旋转机械轴承振动信号分析方法研究[D]. 重庆: 重庆大学, 2014.

    PENG C. Vibration signal analysis of bearings in the rotating mechanical[D]. Chongqing: Chongqing University, 2014 (in Chinese).
    [3] 何洋洋, 王馨怡, 董晶. 基于经验小波变换与谱峭度的船舶轴系故障特征提取方法[J]. 中国舰船研究, 2020, 15(增刊1): 98–106.

    HE Y Y, WANG X Y, DONG J. Fault feature extraction method for marine shafting based on empirical wavelet transform-spectral kurtosis[J]. Chinese Journal of Ship Research, 2020, 15(Supp1): 98–106 (in Chinese).
    [4] CHEN Y L, ZHANG P L. Bearing fault detection based on SVD and EMD[J]. Applied Mechanics and Materials, 2012, 184/185: 70–74. doi: 10.4028/www.scientific.net/AMM.184-185.70
    [5] 张仲良, 朱晓军, 彭飞, 等. 基于HHT的船体结构应力监测数据特征分析和去噪方法[J]. 中国舰船研究, 2019, 14(增刊1): 158–164.

    ZHANG Z L, ZHU X J, PENG F, et al. Characteristic analysis and de-noising method of stress monitoring data of hull structures based on HHT[J]. Chinese Journal of Ship Research, 2019, 14(Supp1): 158–164 (in Chinese).
    [6] 张永祥, 朱杰平, 张帅. 基于EEMD的滚动轴承故障诊断方法[J]. 海军工程大学学报, 2014(6): 90–94.

    ZHANG Y X, ZHU J P, ZHANG S. Rolling element bearing feature extraction based on EEMD[J]. Journal of Naval University of Engineering, 2014(6): 90–94 (in Chinese).
    [7] 唐贵基, 王晓龙. 变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J]. 振动工程学报, 2016, 29(4): 638–648.

    TANG G J, WANG X L. Variational mode decomposition method and its application on incipient fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering, 2016, 29(4): 638–648 (in Chinese).
    [8] 王海龙, 夏筱筠, 孙维堂. 基于EMD与卷积神经网络的滚动轴承故障诊断[J]. 组合机床与自动化加工技术, 2019(10): 46–48, 52.

    WANG H L, XIA X J, SUN W T. Rolling bearing fault diagnosis based on EMD and convolutional neural network[J]. Modular Machine Tool and Automatic Manufacturing Technique, 2019(10): 46–48, 52 (in Chinese).
    [9] 何江江, 李孝全, 赵玉伟, 等. 基于改进EEMD的卷积神经网络滚动轴承故障诊断[J]. 重庆大学学报, 2020, 43(1): 82–89.

    HE J J, LI X Q, ZHAO Y W, et al. Fault diagnosis of rolling bearing based on improved EEMD and convolutional neural network[J]. Journal of Chongqing University, 2020, 43(1): 82–89 (in Chinese).
    [10] 吕阳, 廖与禾, 王报祥, 等. 基于VMD和CNN的滚动轴承故障定量诊断方法[J]. 中国科技论文, 2020, 15(7): 735–742. doi: 10.3969/j.issn.2095-2783.2020.07.020

    LYU Y, LIAO Y H, WANG B X, et al. Quantitative diagnosis method for rolling bearing faults based on VMD and CNN[J]. China Sciencepaper, 2020, 15(7): 735–742 (in Chinese). doi: 10.3969/j.issn.2095-2783.2020.07.020
    [11] 方立华. 基于振动特征的风电机组传动系统机械故障诊断研究[D]. 吉林: 东北电力大学, 2018.

    FANG L H. Research on mechanical fault diagnosis of wind turbine transmission system based on vibration feature[D]. Jilin: Northeast Electric Power University, 2018 (in Chinese).
    [12] GARDNER W A, BROWN W, CHEN C K. Spectral correlation of modulated signals: Part II-digital modulation[J]. Communications IEEE Transactions on, 1987, 35(6): 595–601. doi: 10.1109/TCOM.1987.1096816
    [13] ANTONI J, XIN G, HAMZAOUI N. Fast computation of the spectral correlation[J]. Mechanical Systems & Signal Processing, 2017, 92: 248–277.
    [14] 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 D N, ZHANG Y D, YAO C Y, 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 (in Chinese).
    [15] 薛红新. 基于机器学习方法的分类与预测问题研究[D]. 太原: 中北大学, 2019.

    XUE H X. Research on the problems of classification and prediction based on machine learning methods[D]. Taiyuan: North University of China, 2019 (in Chinese).
    [16] 陈伟南, 黄连忠, 张勇, 等. 基于BP神经网络的船舶主机能效状态评估[J]. 中国舰船研究, 2018, 13(4): 127–133, 160.

    CHEN W N, HUANG L Z, ZHANG Y, et al. Evaluation of main engine energy efficiency based on BP neural network[J]. Chinese Journal of Ship Research, 2018, 13(4): 127–133, 160 (in Chinese).
    [17] AZIZ W, ARIF M. Multiscale permutation entropy of physiological time series[C]//International Multitopic Conference. IEEE, 2005.
    [18] 杨武俊. 基于神经网络的人脸图像识别算法[J]. 网络安全技术与应用, 2016(1): 98,100.

    YANG W J. Face image recognition algorithm based on neural network[J]. Network Security Technology and Application, 2016(1): 98,100 (in Chinese).
    [19] 王靖, 陈特放, 黄采伦. 基于等角度采样的列车频带变化类故障诊断方法研究[J]. 中国机械工程, 2012, 23(16): 1957–1961. doi: 10.3969/j.issn.1004-132X.2012.16.015

    WANG J, CHEN T F, HUANG C L. Research on diagnosis method to train's vibration frequency band—varying fault based on uniform angle sampling[J]. China Mechanical Engineering, 2012, 23(16): 1957–1961 (in Chinese). doi: 10.3969/j.issn.1004-132X.2012.16.015
    [20] URBANEK J, BARSZCZ T, ANTONI J. A two-step procedure for estimation of instantaneous rotational speed with large fluctuations[J]. Mechanical Systems and Signal Processing, 2013, 38(1): 96–102. doi: 10.1016/j.ymssp.2012.05.009
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出版历程
  • 收稿日期:  2020-10-28
  • 修回日期:  2021-03-01
  • 网络出版日期:  2021-05-26

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