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Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines

机译：批归一化的深度神经网络可实现机器的快速智能故障诊断

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摘要

Numerous researches have been conducted on developing effective intelligent fault diagnosis systems. As a commonly used deep learning technique, stacked autoencoders (SAEs) have shown the ability of automatic feature extraction and classification. However, the traditional SAEs have two deficiencies: (1) The multi-layer structure and too many epoch number always require plenty of time for training. (2) The internal covariate shift problem exists in deep networks, leading to that it is hard to train the model with saturating nonlinearities. To overcome the aforementioned deficiencies, a recently developed optimization method called batch normalization is introduced into deep neural networks (DNNs). The method is employed in every layer of DNNs to obtain a steady distribution of activation values during training. Besides, it applies normalization technique on every mini-batch training. As a result, it offers an easy starting condition for training, and the training epoch number can also be reduced. Thus, fault features can be extracted rapidly in an elegant way. A bearing and a gearbox datasets are adopted to conform the effectiveness of the proposed method. The experimental results show that the proposed method can not only solve the two deficiencies of SAEs, but also achieve a superior performance to the existing methods. (C) 2018 Elsevier B.V. All rights reserved.
机译：在开发有效的智能故障诊断系统方面已经进行了许多研究。作为一种常用的深度学习技术，堆叠式自动编码器（SAE）已显示出自动特征提取和分类的能力。但是，传统的SAE有两个缺点：（1）多层结构和过多的纪元数总是需要大量的时间来训练。 （2）深度网络中存在内部协变量偏移问题，导致难以训练具有饱和非线性的模型。为了克服上述缺陷，最近开发的称为批处理规范化的优化方法被引入到深度神经网络（DNN）中。该方法在DNN的每一层中都采用，以在训练过程中获得激活值的稳定分布。此外，它在每次小批量训练中均采用归一化技术。结果，它提供了易于训练的开始条件，并且还可以减少训练时期。因此，可以以优雅的方式快速提取故障特征。采用轴承和齿轮箱数据集来验证所提出方法的有效性。实验结果表明，该方法不仅可以解决SAE的两个缺陷，而且可以取得比现有方法更好的性能。 （C）2018 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2019年第15期|53-65|共13页
  • 作者

    Wang Jinrui; Li Shunming; An Zenghui; Jiang Xingxing; Qian Weiwei; Ji Shanshan;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing, Jiangsu, Peoples R China;

    Soochow Univ, Sch Urban Rail Transportat, Suzhou, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing, Jiangsu, Peoples R China;

    Qingdao Dingtu Spatioinformat Technol Ltd, Qingdao, 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

    Intelligent fault diagnosis; Deep learning; Stacked autoencoders; Batch normalization;

    机译：智能故障诊断;深度学习;堆叠式自动编码器;批量归一化;
  • 入库时间 2022-08-18 04:14:10

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