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Procedia CIRP v.72, 2018년, pp.1084 - 1087  

A Jointed Signal Analysis and Convolutional Neural Network Method for Fault Diagnosis

Wen, Long (The State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan, 430074, China ) ; Gao, Liang (The State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan, 430074, China ) ; Li, Xinyu (The State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan, 430074, China ) ; Wang, Lihui (Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden ) ; Zhu, Jichu (Wuhan Britain-China International School, Wuhan, 430022, China ) ;
  • 초록  

    Abstract Fault diagnosis plays a vital role in the modern industry. In this research, a joint vibration signal analysis and deep learning method for fault diagnosis is proposed. The vibration signal analysis is a well-established technique for condition monitoring, and deep learning has shown its potential in fault diagnosis. In the proposed method, the time-frequency technique, named as S transform, is applied to transfer the vibration signals to images, and then an improved convolutional neural network (CNN) is applied to classify these images. The results show the proposed method has achieved the significant improvement.


  • 주제어

    Fault diagnosis .   convolutional neural network .   time-frequency technique.  

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