摘要详情
80 / 2025-05-14 20:06:51
Wear Prediction of Engine Sliding Bearings Using Physics-Informed Neural Networks
WearPrediction,Physics-Informed Neural Networks,Equipment Health Management,Interpretability
全文待审
lures, and accurately predicting the wear between
friction pairs is essential for equipment health management.
As diesel engines progressively develop toward higher explosion
pressures and power densities, the working environments of key
components such as crankshaft bearings become increasingly
harsh, making them more prone to wear issues that com
promise engine reliability. Most existing data-driven methods
primarily focus on wear modeling using sparse data, yet such
approaches exhibit limitations in interpretability. To address
this challenge, this paper proposes a physics-informed neural
network approach by integrating the Archard wear law and
typical wear processes as physical constraints into the neural
network framework, thereby enhancing model interpretability.
Experimental results demonstrate that incorporating prior
physical knowledge improves the overall performance of the
neural network. Furthermore, parameter sensitivity analysis
reveals that the model maintains behavior consistent with
typical wear processes even when applied to unseen data.
重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月15日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
