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首页> 外文期刊>Journal of the Franklin Institute >An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks
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An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks

机译：使用STFT和生成神经网络滚动轴承的无监督故障诊断方法

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摘要

In recent years, the technique of machine learning or deep learning has been employed in intelligent fault diagnosis methods to achieve much success using massive labeled data. However, it is generally difficult or expensive to label the monitoring data in practical engineering due to its complex working conditions. Therefore, an unsupervised fault diagnosis method is proposed in this paper for rolling bearings, which incorporates short-time Fourier transform (STFT) as well as categorical generative adversarial networks (CatGAN). The proposed method first adopts STFT to transform raw 1-D vibration signals into 2-D time-frequency maps to serve as the input of CatGAN. Then, it obtains a CatGAN model via an adversarial training process to generate fake samples with a similar distribution to the maps extracted by STFT and cluster the input samples into certain categories. Furthermore, the performance of the proposed ST-CatGAN method is verified using a classic rotating machinery dataset, and the experimental results demonstrate its high diagnosis accuracy and strong robustness against the motor load changes. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译：近年来，机器学习技术或深度学习的技术已经采用智能故障诊断方法，以实现大量标记数据的成功。然而，由于其复杂的工作条件，在实际工程中标记监控数据通常是困难或昂贵的。因此，本文提出了一种无监督的故障诊断方法，用于滚动轴承，该滚动轴承包括短时傅里叶变换（STFT）以及分类生成的对抗网络（CATGAN）。所提出的方法首先采用STFT来将原始的1-D振动信号转换为2-D时频贴图，以用作CATGAN的输入。然后，它通过对抗性培训过程获得Catgan模型，以生成具有与STFT提取的地图类似分布的虚假样本，并将输入样本集聚到某些类别中。此外，使用经典旋转机械数据集来验证所提出的ST-CataGaN方法的性能，实验结果表明其高诊断精度和对电动机负荷变化的强大鲁棒性。 （c）2020富兰克林学院。 elsevier有限公司出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2020年第11期|7286-7307|共22页
  • 作者

    Tao Hongfeng; Wang Peng; Chen Yiyang; Stojanovic Vladimir; Yang Huizhong;

  • 作者单位

    Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China;

    Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China;

    Univ Southampton Dept Civil Maritime & Environm Engn Southampton SO16 7QF Hants England;

    Univ Kragujevac Dept Automat Control Robot & Fluid Tech Fac Mech & Civil Engn Kraljevo 36000 Serbia;

    Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China;

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