Aero-engine gas path system health assessment based on depth digital twin. (December 2022)

• Record Type: Journal Article
• Authors: Zhou, Liang; Wang, Huawei; Xu, Shanshan
• Is Part Of: Engineering failure analysis. Volume 142(2022)
• Publication Date: 2022-12
• Subjects: Aero-engine -- Depth digital twin -- Multi-scale simulation -- Data-driven -- Health assessment; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106790
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Combining simulation technology with deep learning method, a depth digital twin model that can accurately characterize the operating state of aero-engine gas path system is constructed. The visualization of the depth digital twin model can intuitively reflect the operating state of the aero-engine gas path system. The proposed MultiScale1DCNN health assessment model improves the feature extraction ability of one-dimensional data and the accuracy of health assessment. The proposed health assessment method based on depth digital twin has strong timeliness, which can evaluate the health status of aero-engine in real time. Abstract: Aero-engine health assessment is of great significance for accurately understanding the health status of aircraft, supporting maintenance decision-making and ensuring flight safety. However, aero-engine has the characteristics of complex structure, fault coupling and state nonlinearity, coupled with the constraints of many factors such as acquisition means, analysis methods and the limitation of abnormal data. It is difficult to obtain a mapping relationship that fully characterizes its operating status through monitoring information. Therefore, this paper proposes a health assessment method based on depth digital twin, which can be used for real-time monitoring of aero-engine operation state. Firstly, the mechanism model is constructed for the multi-scale simulation of aero-engine gas path system. Combined with the advantages of dynamic learning and self-optimization of deep learning method, the data-driven model for data prediction is constructed, and the two are fused to realize the depth digital twin of aero-engine. Then, the digital twin model is used to simulate the high-dimensional monitoring data generated during the operation of aero-engine. Finally, a multi-scale one-dimensional convolution neural network model (MultiScale1DCNN) is proposed to analyze the simulated data, so as to assess the real-time health status of aero-engine. Through the simulation test of aero-engine sensor data, it is verified that the digital twin model has high reliability. Compared with the traditional simulation model, it has higher accuracy. In the aero-engine health assessment tests, the MultiScale1DCNN model can accurately identify the failure mode and assess the failure level, and has high assessment accuracy. In several assessment tests, the assessment accuracy rate is above 96%. The test results show that the health assessment method can accurately reflect the health status of aero-engine, and has certain real-time performance, which shows that it has high engineering application value.