Digital twin-driven intelligent assessment of gear surface degradation

降级(电信) 汽车工程 过程(计算) 催交 根本原因 可靠性工程 传输(电信) 机制(生物学) 工程类 计算机科学 电子工程 系统工程 认识论 操作系统 电气工程 哲学
作者
Ke Feng,Jinchen Ji,Yongchao Zhang,Qing Ni,Zheng Liu,Michael Beer
出处
期刊:Mechanical Systems and Signal Processing [Elsevier BV]
卷期号:186: 109896-109896 被引量:298
标识
DOI:10.1016/j.ymssp.2022.109896
摘要

Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.
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