Existing fault diagnosis methods based on signal processing rely on manual feature extraction, the deep learning method can not learn the time domain and frequency domain characteristics of vibration signal at the same time. The time domain and frequency domain can be considered by short-time Fourier transform. Based on this principle and the Gate Recurrent Unit network (GRU) and Short time Fourier transform (STFT), this paper propose an improved GRU fault diagnosis model. The model can learn the characteristics of both time and frequency domain directly from the original vibration signal, and realize the end-to-end fault diagnosis. Using the Western Reserve University Bearing Data (CWRU) for training and testing, the test accuracy can reach 98.6%. Finally, compared with the traditional LSTM and GRU, the effectiveness and advantages of the improved model are proved.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Rolling Bearing Fault Diagnosis Based on Improved GRU


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liang, Jianying (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Ren, Xiangyu (author) / Qin, Yong (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Publication date :

    2022-02-23


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Rolling Bearing Fault Diagnosis Based on Improved GRU

    Ren, Xiangyu / Qin, Yong | British Library Conference Proceedings | 2022


    Rolling Bearing Fault Diagnosis Based on Improved GRU

    Ren, Xiangyu / Qin, Yong | TIBKAT | 2022



    A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing

    Xu, Tao / Lv, Huan / Lin, Shoujin et al. | SAGE Publications | 2023


    The Fault Diagnosis Method of Rolling Bearing Based on CEEMDAN

    Liu, Libo / Tong, Qingbin | British Library Conference Proceedings | 2022