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Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

机译：基于STFT-深度学习和声音信号的滚动轴承故障诊断

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摘要

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.
机译：故障诊断的主要挑战在于找到良好的故障特征。深度学习网络具有以无人监督的方式自动从输入数据中学习良好特征的能力，其独特的分层预训练和使用反向传播策略的微调可以解决训练深度多层网络的难题。堆叠式稀疏自动编码器或其他深层体系结构在语音识别，面部识别，文本分类，图像识别和其他应用领域中表现出出色的性能。然而，到目前为止，关于故障诊断中的深度学习的研究很少。本文提出了一种基于短时傅立叶变换和堆叠式稀疏自动编码器的滚动轴承故障诊断新方法。此方法分析声音信号。通过短时傅立叶变换得到频谱图后，采用堆叠式稀疏自动编码器自动提取故障特征，采用softmax回归作为故障模式分类的方法。将该方法应用于从滚动轴承试验台获得的声音信号时，将其与经验模态分解，Teager能量算子和堆叠稀疏自动编码器（使用振动信号验证该方法的性能和有效性）进行了比较。

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  • 来源
    《Shock and vibration》 |2016年第6期|6127479.1-6127479.12|共12页
  • 作者

    Liu Hongmei; Li Lianfeng; Ma Jian;

  • 作者单位

    Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China;

    Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China;

    Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China|Sci & Technol Reliabil & Environm Engn Lab, Beijing, 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
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