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A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

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  • Yongbo Li
  • Xianzhi Wang
  • Shubin Si
  • Xiaoqiang Du

Abstract

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

Suggested Citation

  • Yongbo Li & Xianzhi Wang & Shubin Si & Xiaoqiang Du, 2019. "A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography," Complexity, Hindawi, vol. 2019, pages 1-12, August.
    • Handle: RePEc:hin:complx:2619252
    DOI: 10.1155/2019/2619252
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    References listed on IDEAS

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    1. Kaiwei Liang & Na Qin & Deqing Huang & Yuanzhe Fu, 2018. "Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie," Complexity, Hindawi, vol. 2018, pages 1-13, October.
    2. Xihui Chen & Liping Peng & Gang Cheng & Chengming Luo, 2019. "Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN," Complexity, Hindawi, vol. 2019, pages 1-12, March.
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    Cited by:

    1. Jianmin Zhou & Lulu Liu & Xiwen Shen, 2023. "SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection," Mathematics, MDPI, vol. 11(17), pages 1-18, August.

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