Abstract A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
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A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

机译：深度学习技术构建的神经网络及其在机器智能故障诊断中的应用

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摘要

AbstractIn traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken for the manual design of fault features, which makes these methods less automatic. Among deep learning techniques, autoencoders may be a potential tool for automatic feature extraction of mechanical signals. However, traditional autoencoders have two following shortcomings. (1) They may learn similar features in mechanical feature extraction. (2) The learned features have shift variant properties, which leads to the misclassification of mechanical health conditions. To overcome the aforementioned shortcomings, a local connection network (LCN) constructed by normalized sparse autoencoder (NSAE), namely NSAE-LCN, is proposed for intelligent fault diagnosis. We construct LCN by input layer, local layer, feature layer and output layer. When raw vibration signals are fed to the input layer, LCN first uses NSAE to locally learn various meaningful features from input signals in the local layer, then obtains shift-invariant features in the feature layer and finally recognizes mechanical health conditions in the output layer. Thus, NSAE-LCN incorporates feature extraction and fault recognition into a general-purpose learning procedure. A gearbox dataset and a bearing dataset are used to validate the performance of the proposed NSAE-LCN. The results indicate that the learned features of NSAE are meaningful and dissimilar, and LCN helps to produce shift-invariant features and recognizes mechanical health conditions effectively. Through comparing with commonly used diagnosis methods, the superiority of the proposed NSAE-LCN is verified.
机译： 摘要 在传统的机器智能故障诊断方法中，手动设计故障特征会花费大量的实际精力，这使这些方法不太自动化。在深度学习技术中，自动编码器可能是自动提取机械信号特征的潜在工具。然而，传统的自动编码器具有以下两个缺点。 （1）他们可能会在机械特征提取中学习类似的特征。 （2）学习到的特征具有变位特性，从而导致机械健康状况的错误分类。为了克服上述缺点，提出了一种由标准化稀疏自动编码器（NSAE）构成的本地连接网络（NSAE-LCN）用于智能故障诊断。我们通过输入层，局部层，特征层和输出层构造LCN。当原始振动信号被馈送到输入层时，LCN首先使用NSAE从本地层的输入信号中本地学习各种有意义的特征，然后在特征层中获取位移不变特征，最后识别输出层中的机械健康状况。因此，NSAE-LCN将特征提取和故障识别合并到通用学习过程中。使用齿轮箱数据集和轴承数据集来验证所提出的NSAE-LCN的性能。结果表明，NSAE的学习特征是有意义且不相似的，而LCN有助于产生位移不变特征并有效识别机械健康状况。通过与常用的诊断方法进行比较，验证了所提出的NSAE-LCN的优越性。

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  • 来源
    《Neurocomputing》 |2018年第10期|619-628|共10页
  • 作者

    Feng Jia; Yaguo Lei; Liang Guo; Jing Lin; Saibo Xing;

  • 作者单位

    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University;

    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University;

    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University;

    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University;

    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University;

  • 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

    Normalized sparse autoencoder; Deep learning; Intelligent fault diagnosis; Local connection network;

    机译：标准化稀疏自动编码器;深度学习;智能故障诊断;本地连接网络;
  • 入库时间 2022-08-18 02:05:26

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