TY - CONF
T1 - Multilayer Gated Recurrent Unit for Spur Gear Fault Diagnosis
AU - Tao, Ying
AU - Wang, Xiaodan
AU - Sánchez, René Vinicio
AU - Yang, Shuai
AU - Li, Chuan
PY - 2019/7/12
Y1 - 2019/7/12
N2 - As an important transmission component, the spur gearbox may cause great losses if it fails, so fault diagnosis is the key to ensure the normal operation of the equipment. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed, which uses a three-layer gated recurrent unit (GRU) to deal with the fault diagnosis of the spur gear. Due to the complexity of the spur gearbox fault, vibration measurement is carried out separately at first. Then the vibration signals are extracted from the time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. By using this model, fault features can be deeply learned layer by layer, and feature types can be identified with higher accuracy. The proposed method was applied to two spur gears (number of teeth Gear1= 53, and Gear2= 80), which were installed on the input and the output shafts of the gearbox, and there are 10 state modes in total. To evaluate the method's performance, four methods were applied to compare, which are (GRU, multilayer long short-term memory (MLSTM), long short-term memory (LSTM) and support vector machine (SVM)) respectively. The classification result of MGRU model shows that it is effective for spur gear fault diagnosis.
AB - As an important transmission component, the spur gearbox may cause great losses if it fails, so fault diagnosis is the key to ensure the normal operation of the equipment. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed, which uses a three-layer gated recurrent unit (GRU) to deal with the fault diagnosis of the spur gear. Due to the complexity of the spur gearbox fault, vibration measurement is carried out separately at first. Then the vibration signals are extracted from the time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. By using this model, fault features can be deeply learned layer by layer, and feature types can be identified with higher accuracy. The proposed method was applied to two spur gears (number of teeth Gear1= 53, and Gear2= 80), which were installed on the input and the output shafts of the gearbox, and there are 10 state modes in total. To evaluate the method's performance, four methods were applied to compare, which are (GRU, multilayer long short-term memory (MLSTM), long short-term memory (LSTM) and support vector machine (SVM)) respectively. The classification result of MGRU model shows that it is effective for spur gear fault diagnosis.
KW - fault diagnosis
KW - gated recurrent unit
KW - spur gear
KW - vibration signal
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85070531128&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85070531128&origin=inward
UR - http://www.mendeley.com/research/multilayer-gated-recurrent-unit-spur-gear-fault-diagnosis
U2 - 10.1109/PHM-Paris.2019.00023
DO - 10.1109/PHM-Paris.2019.00023
M3 - Paper
SP - 90
EP - 95
T2 - Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Y2 - 1 May 2019
ER -