Fault Diagnosis of Hydraulic Servo Valve Based on a Hybrid Digital Twin

Na Liang, Zhaohui Yuan, Jian Kang

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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..

源语言英语
主期刊名IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings
出版商IEEE Computer Society
ISBN(电子版)9781665464543
DOI
出版状态已出版 - 2024
活动50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024 - Chicago, 美国
期限: 3 11月 20246 11月 2024

出版系列

姓名IECON Proceedings (Industrial Electronics Conference)
ISSN(印刷版)2162-4704
ISSN(电子版)2577-1647

会议

会议50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024
国家/地区美国
Chicago
时期3/11/246/11/24

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