scholarly journals Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks

2022 ◽  
Vol 162 ◽  
pp. 107996
Author(s):  
Junchuan Shi ◽  
Dikang Peng ◽  
Zhongxiao Peng ◽  
Ziyang Zhang ◽  
Kai Goebel ◽  
...  
2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  

Author(s):  
Xiaotong Tu ◽  
Yue Hu ◽  
Fucai Li

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.


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