基于改进堆叠式循环神经网络的轴承故障诊断
CSTR:
作者:
中图分类号:

TP277

基金项目:

国家自然科学基金(51375345)


Bearing Fault Diagnosis Based on Improved Stacked Recurrent Neural Network
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [17]
  • |
  • 相似文献 [20]
  • |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    提出基于改进堆叠式循环神经网络的轴承故障诊断模型.利用深层网络极强的非线性拟合能力以及循环神经网络特有的沿时间通道传播的特点,通过门控循环单元解决堆叠式循环神经网络梯度消失的问题,实现对轴承健康状况的分类识别.利用美国凯斯西储大学轴承数据集进行了轴承故障诊断试验,同时将支持向量机、粒子群优化的支持向量机、人工神经网络、卷积神经网络AlexNet以及循环神经网络作为对比以检验所提模型的分类性能.结果表明,提出的模型能够对轴承故障进行有效诊断,并且具有一定的可靠性与泛化能力.

    Abstract:

    A bearing fault diagnosis model based on improved stacked recurrent neural network was proposed, which takes advantage of great nonlinear fitting capability and the characteristics of propagation though time. Gated recurrent unit was used to deal with the vanishing gradient problem, which contributes to classify the bearing health condition. The data set from Bearing Data Center of Case Western Reserve University was used to carry out the bearing fault diagnosis test. Support vector machine, particle swarm optimization-support vector machine, back-propagation network, AlexNet, and recurrent neural network were tested as well for comparison. The results show that the proposed model has exceptional reliability and generalization.

    参考文献
    [1] ZHANG Jianjun, SONG Yexin, QU Yong. A time series analysis and neural network based scheme for fault diagnosis of transformers[J]. Applied Mechanics Materials, 2015, 742: 412-418.
    [2] YU Jianbo. Health condition monitoring of machines based on Hidden Markov Model and contribution analysis[J]. IEEE Transactions on Instrumentation Measurement, 2012, 61(8): 2200-2211.
    [3] 王舒玮. 小波变换在滚动轴承故障信号分析中的应用[J]. 山西大同大学学报(自然科学版), 2018, (1): 69-71.WANG Shuwei. Application of wavelet transform in fault signal analysis of rolling bearings[J]. Journal of Shanxi Datong University (Natural Science), 2018, (1): 69-71.
    [4] 陶洁, 刘义伦, 付卓,等. 基于Teager能量算子和深度置信网络的滚动轴承故障诊断[J]. 中南大学学报(自然科学版), 2017, 48(1): 61-68.TAO Jie, LIU Yilun, FU Zhuo, et al. Fault damage degrees diagnosis for rolling bearing based on Teager energy operator and deep belief network[J]. Journal of Central South University (Science and Technology), 2017, 48(1): 61-68.
    [5] SHEN Changqing, HE Qingbo, KONG Fangran, et al. A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis[J]. Proceedings of Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 2013, 227(6): 1362-1370.
    [6] JANSSENS O, SLAVKOCIKJ V, VERVISCH B, et al. Convolutional neural network based fault detection for rotating machinery[J]. Journal of Sound Vibration, 2016, 377: 331-345.
    [7] 侯文擎, 叶鸣, 李巍华. 基于改进堆叠降噪自编码的滚动轴承故障分类[J]. 机械工程学报, 2018, 54(7):87-96.HOU Wenqing, YE Ming, LI Weihua. Rolling element bearing fault classification using imporved stacked de-nosing auto-encoders[J]. Journal of Mechanical Engineering, 2018, 54(7):87-96.
    [8] WANG Fan, JIANG Hongkai, SHAO Haidong et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis[J]. Measurement Science Technology, 2017, 28(9).
    [9] 庄夏. 基于DWT和RNN的无刷直流电动机轴承故障检测方法[J]. 微特电机, 2017, 45(6):17-26.ZHUANG Xia. Bearing fault detection of brushless DC motor based on DWT and RNN[J]. Small Special Electrical Machines, 2017, 45(6):17-26.
    [10] CUI Qiangqiang, LI Zhiheng, YANG Jun et al. Rolling bearing fault prognosis using recurrent neural network[C]// Control and Decision Conference. IEEE, 2017:1196-1201.
    [11] 陈再发, 刘彦呈, 刘厶源. 长短期记忆神经网络在机械状态预测中的应用[J]. 大连海事大学学报, 2018, 44(1): 85-90.CHEN Zaifa, LIU Yancheng, LIU Siyuan. Application of long-short term memory neural network in prediction of mechanical state[J]. Journal of Dalian Maritime University, 2018, 44(1): 85-90.
    [12] D''Informatique D E, Ese N, Esent P, et al. Long Short-Term Memory in Recurrent Neural Networks[J]. Epfl, 2001, 9(8): 1735 - 1780.
    [13] CHO K, MERRIENBOER B V, GULCEHRE C, et al. Learning Phrase Representations using RNN Encoder Decoder for Statistical Machine Translation[J]. Computer Science, 2014.
    [14] ASSAAD M, BONE R, CARDOT H. A new boosting algorithm for improved time-series forecasting with recurrent neural networks[J]. Information Fusion, 2008, 9(1): 41-55.
    [15] BENGIO Y, BOULANGER-LEWANDOWSKI N, PASCANU R. Advances in optimizing recurrent networks[C]// IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2013: 8624-8628..
    [16] LOPAROK A. Bearings vibration dataset, case western reserve university[EB/OL]. [2015-06-18]. http:// www.eees.ewru.edu/laboratory/bearing/download.htm.
    [17] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

周奇才,沈鹤鸿,赵炯,刘星辰.基于改进堆叠式循环神经网络的轴承故障诊断[J].同济大学学报(自然科学版),2019,47(10):1500~1507

复制
分享
文章指标
  • 点击次数:1233
  • 下载次数: 912
  • HTML阅读次数: 1333
  • 引用次数: 0
历史
  • 收稿日期:2018-07-30
  • 最后修改日期:2019-07-25
  • 录用日期:2019-06-24
  • 在线发布日期: 2019-10-17
文章二维码