TY - JOUR
T1 - Spur Gear Fault Diagnosis Using a Multilayer Gated Recurrent Unit Approach with Vibration Signal
AU - Tao, Ying
AU - Wang, Xiaodan
AU - Sanchez, Rene Vinicio
AU - Yang, Shuai
AU - Bai, Yun
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The fault diagnosis of the gearbox is a complex and important work. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed for spur gear fault diagnosis, that is, three-layer gated recurrent unit (GRU). The vibration signals are firstly monitored on the test bench, and then extracted in both time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. The MGRU can improve the representation of information and identify the features of fault types more precisely with the increasing number of layers. The proposed method was tested by two spur gears with 10 state modes. To evaluate the method's classification accuracy, four methods were utilized for comparison, i.e., the GRU, long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM), respectively. In addition, the separability and robustness analysis are also discussed for the proposed MGRU performance. All of the results exhibited that the proposed MGRU approach is effective for spur gear fault diagnosis.
AB - The fault diagnosis of the gearbox is a complex and important work. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed for spur gear fault diagnosis, that is, three-layer gated recurrent unit (GRU). The vibration signals are firstly monitored on the test bench, and then extracted in both time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. The MGRU can improve the representation of information and identify the features of fault types more precisely with the increasing number of layers. The proposed method was tested by two spur gears with 10 state modes. To evaluate the method's classification accuracy, four methods were utilized for comparison, i.e., the GRU, long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM), respectively. In addition, the separability and robustness analysis are also discussed for the proposed MGRU performance. All of the results exhibited that the proposed MGRU approach is effective for spur gear fault diagnosis.
KW - Fault diagnosis
KW - gated recurrent unit
KW - robustness
KW - separability
KW - vibration
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065871501&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85065871501&origin=inward
UR - http://www.mendeley.com/research/spur-gear-fault-diagnosis-using-multilayer-gated-recurrent-unit-approach-vibration-signal
U2 - 10.1109/ACCESS.2019.2914181
DO - 10.1109/ACCESS.2019.2914181
M3 - Article
VL - 7
SP - 56880
EP - 56889
JO - IEEE Access
JF - IEEE Access
M1 - 8703047
ER -