...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neurocomputing >An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis
【24h】

An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis

机译：一种改进的深度卷积神经网络，具有用于轴承故障诊断的多尺度信息

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In recent years, deep learning technique has been used in mechanical intelligent fault diagnosis and it has achieved much success. Among the deep learning models, convolutional neural network (CNN) is able to accomplish the feature learning without priori knowledge and pattern classification automatically, which makes it to be an end-to-end method. However, CNN may fall into local optimum when lack of useful information in the input signal. Diversity resolution expressions of signal in frequency domain can be obtained by using the filters with different scales (lengths) and more expressions may provide more useful information. Thus, in this paper, an improved CNN named multi-scale cascade convolutional neural network (MC-CNN) is proposed for the classification information enhancement of input. In MC-CNN, a new layer has been added before convolutional layers to construct a new signal of more distinguishable information. The composed signal is obtained by concatenating the signals convolved by original input and kernels of different lengths. To reduce the abundant neurons produced by the multi-scale signal, a convolutional layer with kernels of small size and a pooling layer are added after the multi-scale cascade layer. To verify the proposed method, the original CNNs and MC-CNN are applied to the pattern classification of bearing vibration signal with four conditions under normal and noise environments, respectively. The classification results show that the proposed MC-CNN is more effective than the commonly CNNs. In addition, the lower t-distributed stochastic neighbor embedding (t-SNE) clustered error verifies the effectiveness and necessity of MC layer further. By analyzing the kernels learned from the multi-scale cascade layer, it can be found that the kernels act as filters of different resolutions to make the frequency domain structure of different fault signals more distinguishable. By studying the influence of kernel scale in MC layer on fault diagnosis, it is found that the optimal scale does exist and will be a research emphasis in the future. Moreover, the effectiveness of MC-CNN is verified furthermore by analyzing the application of MC-CNN in bearing fault diagnosis under nonstationary working conditions. (C) 2019 Elsevier B.V. All rights reserved.
机译：近年来，深入学习技术已用于机械智能故障诊断，成功取得了大量成功。在深度学习模型中，卷积神经网络（CNN）能够自动完成特征学习，而无需先验知识和模式分类，这使得成为端到端的方法。然而，当输入信号中缺乏有用的信息时，CNN可能属于局部最佳。通过使用具有不同刻度（长度）的滤波器可以获得频域中信号中的信号的分集分辨率表达，并且更多的表达式可以提供更有用的信息。因此，在本文中，提出了一种名为多级级联卷积神经网络（MC-CNN）的改进的CNN，用于输入输入的分类信息。在MC-CNN中，在卷积层之前已经添加了一种新层，以构建更可区分信息的新信号。通过连接由原始输入和不同长度的核来连接的信号来获得组合信号。为了减少多尺度信号产生的丰富神经元，在多尺寸级联层之后，添加具有小尺寸和池池的粒的卷积层。为了验证所提出的方法，分别将原始CNN和MC-CNN应用于轴承振动信号的图案分类，分别在正常和噪声环境下具有四种条件。分类结果表明，所提出的MC-CNN比通常的CNN更有效。此外，较低的T分布式随机邻居嵌入（T-SNE）聚类误差验证了MC层的有效性和必要性。通过分析从多尺度级联层中学到的内核，可以发现内核作为不同分辨率的过滤器，以使不同故障信号的频域结构更可区分。通过研究核尺度在MC层对故障诊断的影响，发现最佳规模存在，并将成为未来的重点。此外，通过分析在非营养工作条件下的轴承故障诊断中的应用，还通过验证MC-CNN的有效性。 （c）2019 Elsevier B.v.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2019年第24期|77-92|共16页
  • 作者

    Huang Wenyi; Cheng Junsheng; Yang Yu; Guo Gaoyuan;

  • 作者单位

    Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China;

    Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China;

    Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China;

    Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Deep learning; Convolutional neural network; Multi-scale cascade; Rolling bearing; Fault diagnosis;

    机译：深度学习;卷积神经网络;多尺度级联;滚动轴承;故障诊断;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号