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基于小波变换和改进卷积神经网络的刚性罐道故障诊断

杜菲 马天兵 胡伟康 吕英辉 彭猛

杜菲,马天兵,胡伟康,等. 基于小波变换和改进卷积神经网络的刚性罐道故障诊断[J]. 工矿自动化,2022,48(9):42-48, 62.  doi: 10.13272/j.issn.1671-251x.17964
引用本文: 杜菲,马天兵,胡伟康,等. 基于小波变换和改进卷积神经网络的刚性罐道故障诊断[J]. 工矿自动化,2022,48(9):42-48, 62.  doi: 10.13272/j.issn.1671-251x.17964
DU Fei, MA Tianbing, HU Weikang, et al. Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network[J]. Journal of Mine Automation,2022,48(9):42-48, 62.  doi: 10.13272/j.issn.1671-251x.17964
Citation: DU Fei, MA Tianbing, HU Weikang, et al. Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network[J]. Journal of Mine Automation,2022,48(9):42-48, 62.  doi: 10.13272/j.issn.1671-251x.17964

基于小波变换和改进卷积神经网络的刚性罐道故障诊断

doi: 10.13272/j.issn.1671-251x.17964
基金项目: 安徽省自然科学基金面上项目(2008085ME178);安徽省重点研究与开发计划项目(202104a07020005);安徽高校自然科学研究项目(KJ2020A0281);安徽高校学科拔尖人才项目(gxbjZD202020063);国家重点实验室资助项目(SKLMRDPC20ZZ01)。
详细信息
    作者简介:

    杜菲(1981—),女,安徽舒城人,副教授,硕士,研究方向为振动测试与故障诊断,E-mail:dufeimtb@163.com

  • 中图分类号: TD53

Fault diagnosis of rigid guide based on wavelet transform and improved convolutional neural network

  • 摘要: 现有刚性罐道故障诊断方法有的仅适用于小样本数据集,有的虽适用于大样本数据集,但忽略了实际工作环境中的多工况背景。基于卷积神经网络的刚性罐道故障诊断方法存在数据和运算量庞大,易产生过拟合等问题。针对上述问题,提出了一种基于小波变换和改进卷积神经网络的刚性罐道故障诊断方法。首先,在刚性罐道设置错位与间隙2种缺陷,采集多工况下提升容器振动加速度信号。其次,利用小波变换将采集的振动加速度信号转换为二维时频图像,采用试凑法最终确定经Complex Morlet 小波基函数处理后的二维时频图像的时间和频率分辨率最佳。然后,通过改进卷积神经网络模型结构,即保留第1层和第5层池化层,将第2,3,4层池化层替换为小尺度卷积层,以防止过拟合现象。最后,将二维时频图像输入改进后的卷积神经网络模型。实验结果表明: ① 改进模型经过训练后,在训练集上的平均准确率为99%左右,在测试集上的平均准确率为99.5%。② 当数据训练至200步后,改进模型的准确率达99%以上,改进模型的损失函数值趋近于0,说明改进模型收敛性能较好,模型的泛化能力得到了增强,在学习过程中对于过拟合的抑制效果明显。③ 在验证集混淆矩阵上,间隙缺陷和错位缺陷识别准确率为100%,无缺陷识别准确率为92%。④ 与EMD−SVD−SVM、小波包−SVM、EMD−SVD−BP神经网络、小波包−BP神经网络相比,基于小波变换和改进卷积神经网络的刚性罐道故障诊断方法准确率达99%。

     

  • 图  1  基于小波变换和改进卷积神经网络的刚性罐道缺陷诊断过程

    Figure  1.  Fault diagnosis process of rigid guide based on wavelet transform and improved convolutional neural network

    图  2  提升系统实验台

    Figure  2.  Hoisting system test bench

    图  3  错位缺陷时振动加速度信号

    Figure  3.  Acceleration signal of vibration during dislocation defect

    图  4  间隙缺陷时振动加速度信号

    Figure  4.  Acceleration signal of vibration during gap defect shock

    图  5  错位缺陷振动加速度信号二维时频图像

    Figure  5.  Two-dimensional time-frequency image of dislocation defect vibration acceleration signal

    图  6  间隙缺陷时振动加速度信号二维时频图像

    Figure  6.  Two-dimensional time-frequency image of gap defect vibration acceleration signal

    图  7  改进后的卷积神经网络模型结构

    Figure  7.  Improved convolutional neural network model structure

    图  8  改进神经网络模型的准确率

    Figure  8.  Accuracy rates of improved convolutional neural network model

    图  9  改进神经网络模型准确率与损失函数值变化趋势

    Figure  9.  Accuracy and loss function values change trend of improved convolutional neural network model

    图  10  验证集结果预测混淆矩阵

    Figure  10.  Validation set result prediction confusion matrix

    图  11  不同方法下缺陷诊断识别准确率

    Figure  11.  Average of defect diagnosis identification under different methods

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出版历程
  • 收稿日期:  2022-06-16
  • 修回日期:  2022-09-05
  • 网络出版日期:  2022-09-15

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