TY - GEN
T1 - Fault Diagnosis of Hydraulic Servo Valve Based on a Hybrid Digital Twin
AU - Liang, Na
AU - Yuan, Zhaohui
AU - Kang, Jian
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Electro-hydraulic servo valve is a complex component integrating machine, electricity, and fluid, which is widely used in aerospace hydraulic system. It is a key component of the hydraulic system, and as a highly reliable and integrated component, faults are often concealed, and acquiring labeled fault samples is challenging. These factors limit the development of efficient fault diagnose based method of data-driven. In this paper, a hybrid digital twin modeling technique combining physical model and data-driven is proposed for electro-hydraulic servo valve fault diagnosis under insufficient or uneven sample size. Firstly, a high-fidelity digital twin model of the servo valve is built by combining virtual simulation based on physical model and generative adversarial network. Then using the built digital twin model, simulated signals under fault conditions are generated to expand the sample size and train the data-driven convolutional neural network-based fault diagnosis model. The experimental results show that the proposed diagnostic framework can solve the problem of the lack of sample size of the hydraulic system and effectively improve the accuracy of fault diagnosis. The proposed combined physical and data-driven digital twin framework can be applied to other hydraulic systems and fields..
AB - Electro-hydraulic servo valve is a complex component integrating machine, electricity, and fluid, which is widely used in aerospace hydraulic system. It is a key component of the hydraulic system, and as a highly reliable and integrated component, faults are often concealed, and acquiring labeled fault samples is challenging. These factors limit the development of efficient fault diagnose based method of data-driven. In this paper, a hybrid digital twin modeling technique combining physical model and data-driven is proposed for electro-hydraulic servo valve fault diagnosis under insufficient or uneven sample size. Firstly, a high-fidelity digital twin model of the servo valve is built by combining virtual simulation based on physical model and generative adversarial network. Then using the built digital twin model, simulated signals under fault conditions are generated to expand the sample size and train the data-driven convolutional neural network-based fault diagnosis model. The experimental results show that the proposed diagnostic framework can solve the problem of the lack of sample size of the hydraulic system and effectively improve the accuracy of fault diagnosis. The proposed combined physical and data-driven digital twin framework can be applied to other hydraulic systems and fields..
KW - GAN
KW - digital twin
KW - fault diagnosis
KW - servo valve
UR - http://www.scopus.com/inward/record.url?scp=105000962734&partnerID=8YFLogxK
U2 - 10.1109/IECON55916.2024.10905952
DO - 10.1109/IECON55916.2024.10905952
M3 - 会议稿件
AN - SCOPUS:105000962734
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings
PB - IEEE Computer Society
T2 - 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024
Y2 - 3 November 2024 through 6 November 2024
ER -